human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "code mixed to monolingual translation framework"):
  Abstract: The use of multilingualism in the new generation is widespread in the form of code-mixed data on social media, and therefore a robust translation system is required for catering to the monolingual users, as well as for easier comprehension by language processing models. In this work, we present a translation framework that uses a translation-transliteration strategy for translating code-mixed data into their equivalent monolingual instances. For converting the output to a more fluent form, it is reordered using a target language model. The most important advantage of the proposed framework is that it does not require a code-mixed to monolingual parallel corpus at any point. On testing the framework, it achieved BLEU and TER scores of 16.47 and 55.45, respectively. Since the proposed framework deals with various sub-modules, we dive deeper into the importance of each of them, analyze the errors and finally, discuss some improvement strategies.

Includes members:
  Member "analyzing roles of classifiers and code mixed factors for sentiment identification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance")
  Member "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance":
    also connected through other hyperedges:
      (none)
  Member "language identification of bengali english code mixed data using character phonetic based lstm models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strategies for language identification in code mixed low resource languages")
  Member "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improved sentiment detection via label transfer from monolingual to synthetic code switched text")
  Member "smt vs nmt a comparison over hindi bengali simple sentences":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "jumt at wmt2019 news translation task a hybrid approach to machine translation for lithuanian to english")
  Member "sentiment identification in code mixed social media text":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "preparing bengali english code mixed corpus for sentiment analysis of indian languages")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "fully character level neural machine translation without explicit segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a brief survey of multilingual neural machine translation")

Question: Please tell me which class the source paper belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.CL(Computation and Language)