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: "text relatedness based on a word thesaurus"
  Abstract: The computation of relatedness between two fragments of text in an automated manner requires taking into account a wide range of factors pertaining to the meaning the two fragments convey, and the pairwise relations between their words. Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words. Such a measure that captures well both aspects of text relatedness may help in many tasks, such as text retrieval, classification and clustering. In this paper we present a new approach for measuring the semantic relatedness between words based on their implicit semantic links. The approach exploits only a word thesaurus in order to devise implicit semantic links between words. Based on this approach, we introduce Omiotis, a new measure of semantic relatedness between texts which capitalizes on the word-to-word semantic relatedness measure (SR) and extends it to measure the relatedness between texts. We gradually validate our method: we first evaluate the performance of the semantic relatedness measure between individual words, covering word-to-word similarity and relatedness, synonym identification and word analogy; then, we proceed with evaluating the performance of our method in measuring text-to-text semantic relatedness in two tasks, namely sentence-to-sentence similarity and paraphrase recognition. Experimental evaluation shows that the proposed method outperforms every lexicon-based method of semantic relatedness in the selected tasks and the used data sets, and competes well against corpus-based and hybrid approaches.

Connected through hyperedge HE-1 (induced by source paper "interactive attention for semantic text matching"):
  with members:
    - "a fast deep learning model for textual relevance in biomedical information retrieval"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "pacrr a position aware neural ir model for relevance matching"
    - "a deep relevance matching model for ad hoc retrieval"
    - "neural information retrieval a literature review"
    - "convolutional neural network architectures for matching natural language sentences"
    - "a deep architecture for semantic matching with multiple positional sentence representations"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-2 (induced by source paper "clac semantic relatedness of words and phrases"):
  with members:
    - "experimental support for a categorical compositional distributional model of meaning"

Connected through hyperedge HE-3 (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"
    - "roget s thesaurus and semantic similarity"
    - "the google similarity distance"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-4 (induced by source paper "human and machine judgements for russian semantic relatedness"):
  with members:
    - "russe the first workshop on russian semantic similarity"
    - "morphological analyzer and generator for russian and ukrainian languages"
    - "simlex 999 evaluating semantic models with genuine similarity estimation"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "a survey of paraphrasing and textual entailment methods"):
  with members:
    - "from frequency to meaning vector space models of semantics"
    - "sentence compression as tree transduction"
    - "acquiring lexical paraphrases from a single corpus"
    - "learning to paraphrase an unsupervised approach using multiple sequence alignment"
    - "bootstrapping lexical choice via multiple sequence alignment"
    - "semantic similarity in a taxonomy an information based measure and its application to problems of ambiguity in natural language"

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)