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 "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance"):
  Abstract: Building tools for code-mixed data is rapidly gaining popularity in the NLP research community as such data is exponentially rising on social media. Working with code-mixed data contains several challenges, especially due to grammatical inconsistencies and spelling variations in addition to all the previous known challenges for social media scenarios. In this article, we present a novel architecture focusing on normalizing phonetic typing variations, which is commonly seen in code-mixed data. One of the main features of our architecture is that in addition to normalizing, it can also be utilized for back-transliteration and word identification in some cases. Our model achieved an accuracy of 90.27% on the test data.

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 "code mixed to monolingual translation framework")
  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 "code mixed to monolingual translation framework")
  Member "automatic normalization of word variations in code mixed social media text":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "towards automation of sense type identification of verbs in ontosensenet telugu")
  Member "preparing bengali english code mixed corpus for sentiment analysis of indian languages":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "strategies for language identification in code mixed low resource languages")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "deepnorm a deep learning approach to text normalization":
    also connected through other hyperedges:
      (none)
  Member "shallow parsing pipeline for hindi english code mixed social media text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "curriculum learning strategies for hindi english codemixed sentiment analysis")
  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)