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: "native language cognate effects on second language lexical choice"
  Abstract: We present a computational analysis of cognate effects on the spontaneous linguistic productions of advanced non-native speakers. Introducing a large corpus of highly competent non-native English speakers, and using a set of carefully selected lexical items, we show that the lexical choices of non-natives are affected by cognates in their native language. This effect is so powerful that we are able to reconstruct the phylogenetic language tree of the Indo-European language family solely from the frequencies of specific lexical items in the English of authors with various native languages. We quantitatively analyze non-native lexical choice, highlighting cognate facilitation as one of the important phenomena shaping the language of non-native speakers.

Connected through hyperedge HE-1 (induced by source paper "fair is better than sensational man is to doctor as woman is to doctor"):
  with members:
    - "on measuring social biases in sentence encoders"
    - "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"
    - "it s all in the name mitigating gender bias with name based counterfactual data substitution"
    - "evaluating the underlying gender bias in contextualized word embeddings"
    - "gender bias in contextualized word embeddings"
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "the geometry of culture analyzing the meanings of class through word embeddings"
    - "deep contextualized word representations"

Connected through hyperedge HE-2 (induced by source paper "topics to avoid demoting latent confounds in text classification"):
  with members:
    - "attention is not not explanation"
    - "saliency driven word alignment interpretation for neural machine translation"
    - "is attention interpretable"
    - "attention is not explanation"
    - "examining gender and race bias in two hundred sentiment analysis systems"
    - "feature wise bias amplification"
    - "adversarial removal of demographic attributes from text data"
    - "which training methods for gans do actually converge"

Connected through hyperedge HE-3 (induced by source paper "say anything automatic semantic infelicity detection in l2 english indefinite pronouns"):
  with members:
    - "assessing bert s syntactic abilities"
    - "multilingual constituency parsing with self attention and pre training"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "targeted syntactic evaluation of language models"
    - "a nested attention neural hybrid model for grammatical error correction"
    - "grammatical error correction with neural reinforcement learning"
    - "exploring the limits of language modeling"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"

Connected through hyperedge HE-4 (induced by source paper "black is to criminal as caucasian is to police detecting and removing multiclass bias in word embeddings"):
  with members:
    - "lipstick on a pig debiasing methods cover up systematic gender biases in word embeddings but do not remove them"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "semantics derived automatically from language corpora contain human like biases"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "efficient estimation of word representations in vector space"
    - "introduction to the conll 2003 shared task language independent named entity recognition"

Connected through hyperedge HE-5 (induced by source paper "towards ethical content based detection of online influence campaigns"):
  with members:
    - "still out there modeling and identifying russian troll accounts on twitter"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "codeswitch reddit exploration of written multilingual discourse in online discussion forums"):
  with members:
    - "named entity recognition on code switched data overview of the calcs 2018 shared task"
    - "building a unified code switching asr system for south african languages"
    - "multilingual constituency parsing with self attention and pre training"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "challenges of computational processing of code switching"

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)