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 as a lexical resource for natural language processing"
  Abstract: WordNet proved that it is possible to construct a large-scale electronic lexical database on the principles of lexical semantics. It has been accepted and used extensively by computational linguists ever since it was released. Inspired by WordNet's success, we propose as an alternative a similar resource, based on the 1987 Penguin edition of Roget's Thesaurus of English Words and Phrases. #R##N#Peter Mark Roget published his first Thesaurus over 150 years ago. Countless writers, orators and students of the English language have used it. Computational linguists have employed Roget's for almost 50 years in Natural Language Processing, however hesitated in accepting Roget's Thesaurus because a proper machine tractable version was not available. #R##N#This dissertation presents an implementation of a machine-tractable version of the 1987 Penguin edition of Roget's Thesaurus - the first implementation of its kind to use an entire current edition. It explains the steps necessary for taking a machine-readable file and transforming it into a tractable system. This involves converting the lexical material into a format that can be more easily exploited, identifying data structures and designing classes to computerize the Thesaurus. Roget's organization is studied in detail and contrasted with WordNet's. #R##N#We show two applications of the computerized Thesaurus: computing semantic similarity between words and phrases, and building lexical chains in a text. The experiments are performed using well-known benchmarks and the results are compared to those of other systems that use Roget's, WordNet and statistical techniques. Roget's has turned out to be an excellent resource for measuring semantic similarity; lexical chains are easily built but more difficult to evaluate. We also explain ways in which Roget's Thesaurus and WordNet can be combined.

Connected through hyperedge HE-1 (induced by source paper "textual membership queries"):
  with members:
    - "automated hate speech detection and the problem of offensive language"
    - "text understanding from scratch"
    - "learning using 1 local membership queries"
    - "distributed representations of words and phrases and their compositionality"
    - "from frequency to meaning vector space models of semantics"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "a sentimental education sentiment analysis using subjectivity summarization based on minimum cuts"

Connected through hyperedge HE-2 (induced by source paper "eve explainable vector based embedding technique using wikipedia"):
  with members:
    - "vector embedding of wikipedia concepts and entities"
    - "query expansion with locally trained word embeddings"
    - "why should i trust you explaining the predictions of any classifier"
    - "a latent variable model approach to pmi based word embeddings"
    - "topic2vec learning distributed representations of topics"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-3 (induced by source paper "semantic sort a supervised approach to personalized semantic relatedness"):
  with members:
    - "roget s thesaurus and semantic similarity"
    - "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"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-4 (induced by source paper "wikipedia based semantic interpretation for natural language processing"):
  with members:
    - "roget s thesaurus and semantic similarity"
    - "similarity of semantic relations"
    - "measuring semantic similarity by latent relational analysis"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "machine learning in automated text categorization"
    - "mining the web for synonyms pmi ir versus lsa on toefl"
    - "thumbs up sentiment classification using machine learning techniques"
    - "unsupervised learning of semantic orientation from a hundred billion word corpus"

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)