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: "geometry of compositionality"
  Abstract: This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way. The test is computationally simple, relying on no external resources and only uses a set of trained word vectors. Experiments show that the proposed method is competitive with state of the art and displays high accuracy in context-specific compositionality detection of a variety of natural language phenomena (idiomaticity, sarcasm, metaphor) for different datasets in multiple languages. The key insight is to connect compositionality to a curious geometric property of word embeddings, which is of independent interest.

Connected through hyperedge HE-1 (induced by source paper "context sensitive malicious spelling error correction"):
  with members:
    - "deep learning for detecting cyberbullying across multiple social media platforms"
    - "impact of content features for automatic online abuse detection"
    - "deceiving google s perspective api built for detecting toxic comments"
    - "neural networks for text correction and completion in keyboard decoding"
    - "polyglot distributed word representations for multilingual nlp"
    - "distributed representations of words and phrases and their compositionality"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-2 (induced by source paper "pare a paper reviewer matching approach using a common topic space"):
  with members:
    - "document similarity for texts of varying lengths via hidden topics"
    - "weakly learning to match experts in online community"
    - "an empirical evaluation of doc2vec with practical insights into document embedding generation"
    - "what the f measure doesn t measure features flaws fallacies and fixes"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "casting a wide net robust extraction of potentially idiomatic expressions"):
  with members:
    - "a challenge set approach to evaluating machine translation"

Connected through hyperedge HE-4 (induced by source paper "representing sentences as low rank subspaces"):
  with members:
    - "geometry of polysemy"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "siamese cbow optimizing word embeddings for sentence representations"
    - "learning distributed representations of sentences from unlabelled data"
    - "a latent variable model approach to pmi based word embeddings"
    - "skip thought vectors"
    - "towards universal paraphrastic sentence embeddings"
    - "aligning books and movies towards story like visual explanations by watching movies and reading books"

Connected through hyperedge HE-5 (induced by source paper "prepositions in context"):
  with members:
    - "linear algebraic structure of word senses with applications to polysemy"
    - "semi supervised preposition sense disambiguation using multilingual data"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "deep learning for answer sentence selection"
    - "distributed representations of words and phrases and their compositionality"

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)