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: "incorporating both distributional and relational semantics in word representations"
  Abstract: We investigate the hypothesis that word representations ought to incorporate both distributional and relational semantics. To this end, we employ the Alternating Direction Method of Multipliers (ADMM), which flexibly optimizes a distributional objective on raw text and a relational objective on WordNet. Preliminary results on knowledge base completion, analogy tests, and parsing show that word representations trained on both objectives can give improvements in some cases.

Connected through hyperedge HE-1 (induced by source paper "integrating lexical knowledge in word embeddings using sprinkling and retrofitting"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep contextualized word representations"
    - "higher order coreference resolution with coarse to fine inference"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "incorporating syntactic and semantic information in word embeddings using graph convolutional networks"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "reporting score distributions makes a difference performance study of lstm networks for sequence tagging"
    - "counter fitting word vectors to linguistic constraints"

Connected through hyperedge HE-2 (induced by source paper "causally denoise word embeddings using half sibling regression"):
  with members:
    - "an empirical study on post processing methods for word embeddings"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "post processing of word representations via variance normalization and dynamic embedding"
    - "semantic specialisation of distributional word vector spaces using monolingual and cross lingual constraints"
    - "all but the top simple and effective postprocessing for word representations"
    - "counter fitting word vectors to linguistic constraints"
    - "simverb 3500 a large scale evaluation set of verb similarity"
    - "neural architectures for named entity recognition"

Connected through hyperedge HE-3 (induced by source paper "leveraging lexical resources for learning entity embeddings in multi relational data"):
  with members:
    - "the mechanism of additive composition"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "take and took gaggle and goose book and read evaluating the utility of vector differences for lexical relation learning"):
  with members:
    - "semeval 2010 task 8 multi way classification of semantic relations between pairs of nominals"
    - "relations such as hypernymy identifying and exploiting hearst patterns in distributional vectors for lexical entailment"
    - "a latent variable model approach to pmi based word embeddings"
    - "random walks on context spaces towards an explanation of the mysteries of semantic word embeddings"
    - "distributional semantics beyond words supervised learning of analogy and paraphrase"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-5 (induced by source paper "a generative model of words and relationships from multiple sources"):
  with members:
    - "improved semantic representations from tree structured long short term memory networks"
    - "exploiting task oriented resources to learn word embeddings for clinical abbreviation expansion"
    - "adam a method for stochastic optimization"
    - "retrofitting word vectors to semantic lexicons"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "embedding entities and relations for learning and inference in knowledge bases"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "encoding prior knowledge with eigenword embeddings"):
  with members:
    - "optimizing spectral learning for parsing"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "similarity of semantic relations"

Connected through hyperedge HE-7 (induced by source paper "non distributional word vector representations"):
  with members:
    - "sparse overcomplete word vector representations"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "retrofitting word vectors to semantic lexicons"
    - "efficient estimation of word representations in vector space"
    - "crowdsourcing a word emotion association lexicon"
    - "colourful language measuring word colour associations"
    - "from frequency to meaning vector space models of semantics"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-8 (induced by source paper "solving verbal comprehension questions in iq test by knowledge powered word embedding"):
  with members:
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "towards ai complete question answering a set of prerequisite toy tasks"
    - "wordrep a benchmark for research on learning word representations"
    - "connecting language and knowledge bases with embedding models for relation extraction"
    - "distributed representations of words and phrases and their compositionality"
    - "automated word puzzle generation via topic dictionaries"
    - "natural language processing almost from scratch"

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)