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: "roget s thesaurus and semantic similarity"
  Abstract: We have implemented a system that measures semantic similarity using a computerized 1987 Roget's Thesaurus, and evaluated it by performing a few typical tests. We compare the results of these tests with those produced by WordNet-based similarity measures. One of the benchmarks is Miller and Charles' list of 30 noun pairs to which human judges had assigned similarity measures. We correlate these measures with those computed by several NLP systems. The 30 pairs can be traced back to Rubenstein and Goodenough's 65 pairs, which we have also studied. Our Roget's-based system gets correlations of .878 for the smaller and .818 for the larger list of noun pairs; this is quite close to the .885 that Resnik obtained when he employed humans to replicate the Miller and Charles experiment. We further evaluate our measure by using Roget's and WordNet to answer 80 TOEFL, 50 ESL and 300 Reader's Digest questions: the correct synonym must be selected amongst a group of four words. Our system gets 78.75%, 82.00% and 74.33% of the questions respectively.

Connected through hyperedge HE-1 (induced by source paper "from word to sense embeddings a survey on vector representations of meaning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "towards better ud parsing deep contextualized word embeddings ensemble and treebank concatenation"
    - "seven augmenting word embeddings with unsupervised relation vectors"
    - "incorporating glosses into neural word sense disambiguation"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "knowledge based word sense disambiguation using topic models"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-2 (induced by source paper "inducing and embedding senses with scaled gumbel softmax"):
  with members:
    - "from word to sense embeddings a survey on vector representations of meaning"
    - "deep contextualized word representations"
    - "wic 10 000 example pairs for evaluating context sensitive representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "probabilistic fasttext for multi sense word embeddings"
    - "a mixture model for learning multi sense word embeddings"
    - "muse modularizing unsupervised sense embeddings"

Connected through hyperedge HE-3 (induced by source paper "a survey of word embeddings evaluation methods"):
  with members:
    - "semantic structure and interpretability of word embeddings"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "an exploration of word embedding initialization in deep learning tasks"
    - "hypothesis testing based intrinsic evaluation of word embeddings"
    - "experiential distributional and dependency based word embeddings have complementary roles in decoding brain activity"
    - "how to evaluate word embeddings on importance of data efficiency and simple supervised tasks"
    - "automated detection of non relevant posts on the russian imageboard 2ch importance of the choice of word representations"
    - "cross lingual models of word embeddings an empirical comparison"

Connected through hyperedge HE-4 (induced by source paper "muse modularizing unsupervised sense embeddings"):
  with members:
    - "rationalizing neural predictions"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient non parametric estimation of multiple embeddings per word in vector space"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "molding cnns for text non linear non consecutive convolutions"
    - "do multi sense embeddings improve natural language understanding"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (induced by source paper "a comprehensive comparative study of word and sentence similarity measures"):
  with members:
    - "using latent semantic analysis to identify quality in use qu indicators from user reviews"
    - "building a pilot software quality in use benchmark dataset"
    - "measuring software quality in use state of the art and research challenges"
    - "text relatedness based on a word thesaurus"
    - "the google similarity distance"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-6 (induced by source paper "unsupervised measure of word similarity how to outperform co occurrence and vector cosine in vsms"):
  with members:
    - "from frequency to meaning vector space models of semantics"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-7 (induced by source paper "semantic measures for the comparison of units of language concepts or entities from text and knowledge base analysis"):
  with members:
    - "description and evaluation of semantic similarity measures approaches"
    - "distributional measures as proxies for semantic relatedness"
    - "distributional measures of semantic distance a survey"
    - "a new similarity measure for taxonomy based on edge counting"
    - "a semantic relatedness measure based on combined encyclopedic ontological and collocational knowledge"
    - "serimi resource description similarity rdf instance matching and interlinking"
    - "hitting and commute times in large graphs are often misleading"
    - "effectively integrating information content and structural relationship to improve the go based similarity measure between proteins"

Connected through hyperedge HE-8 (induced by source paper "semantic sort a supervised approach to personalized semantic relatedness"):
  with members:
    - "active learning using smooth relative regret approximations with applications"
    - "a semantic relatedness measure based on combined encyclopedic ontological and collocational knowledge"
    - "text relatedness based on a word thesaurus"
    - "wikipedia based semantic interpretation for natural language processing"
    - "the google similarity distance"
    - "roget s thesaurus as a lexical resource for natural language processing"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "machine learning in automated text categorization"

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)