human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "universal dependency parsing for hindi english code switching"
  Abstract: Code-switching is a phenomenon of mixing grammatical structures of two or more languages under varied social constraints. The code-switching data differ so radically from the benchmark corpora used in NLP community that the application of standard technologies to these data degrades their performance sharply. Unlike standard corpora, these data often need to go through additional processes such as language identification, normalization and/or back-transliteration for their efficient processing. In this paper, we investigate these indispensable processes and other problems associated with syntactic parsing of code-switching data and propose methods to mitigate their effects. In particular, we study dependency parsing of code-switching data of Hindi and English multilingual speakers from Twitter. We present a treebank of Hindi-English code-switching tweets under Universal Dependencies scheme and propose a neural stacking model for parsing that efficiently leverages part-of-speech tag and syntactic tree annotations in the code-switching treebank and the preexisting Hindi and English treebanks. We also present normalization and back-transliteration models with a decoding process tailored for code-switching data. Results show that our neural stacking parser is 1.5% LAS points better than the augmented parsing model and our decoding process improves results by 3.8% LAS points over the first-best normalization and/or back-transliteration.

Connected through hyperedge HE-1 (induced by source paper "leveraging pretrained word embeddings for part of speech tagging of code switching data"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "part of speech tagging for code switched data"
    - "creating a large multi layered representational repository of linguistic code switched arabic data"
    - "joining hands exploiting monolingual treebanks for parsing of code mixing data"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"
    - "stack propagation improved representation learning for syntax"

Connected through hyperedge HE-2 (induced by source paper "how multilingual is multilingual bert"):
  with members:
    - "bert rediscovers the classical nlp pipeline"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "cross lingual language model pretraining"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "dissecting contextual word embeddings architecture and representation"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "a fast compact accurate model for language identification of codemixed text"):
  with members:
    - "fasttext zip compressing text classification models"
    - "natural language processing with small feed forward networks"
    - "structured training for neural network transition based parsing"

Question: Please tell me which class the center node 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)