# This template is based on the framework supplied for a similar challenge, in a Coursera Data Science course: https://www.coursera.org/course/datasci
# And the code supplied here: https://github.com/uwescience/datasci_course_materials/blob/master/assignment3/wordcount.py
import json
import sys
from collections import OrderedDict
class MapReduce:
    def __init__(self):
        self.intermediate = OrderedDict()
        self.result = []

    def emitIntermediate(self, key, value):
        self.intermediate.setdefault(key, [])
        self.intermediate[key].append(value)

    def emit(self, value):
        self.result.append(value) 

    def execute(self, data, mapper, reducer):
        for line in data:
            record = json.loads(line)
            mapper(record)
        for key in self.intermediate:
            reducer(key, self.intermediate[key])
        return self.result

mapReducer = MapReduce()

def mapper(record):
    key = record["key"]
    value = record["value"]
    # ADD THE REQUIRED LOGIC BELOW
    # You may need to add some lines for the mapper logic to work
    # At the end, you need to complete the emit intermediate step
    mapReducer.emitIntermediate(?, ?)

def reducer(key, list_of_values):
    # ADD THE REQUIRED LOGIC BELOW
    # You may need to add some lines for the reducer logic to work
    # At the end, you need to complete the emit step
    mapReducer.emit(?)

if __name__ == '__main__':
  inputData = []
  nr, ns = map(int, raw_input().split())
  for i in range(nr):
    k = int(raw_input())
    inputData.append(json.dumps({"key":k,"value":"R"}))
  for i in range(ns):
    k = int(raw_input())
    inputData.append(json.dumps({"key":k,"value":"S"}))
  result = mapReducer.execute(inputData, mapper, reducer)
  for n in result:
    print n