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: "watset automatic induction of synsets from a graph of synonyms"
  Abstract: This paper presents a new graph-based approach that induces synsets using synonymy dictionaries and word embeddings. First, we build a weighted graph of synonyms extracted from commonly available resources, such as Wiktionary. Second, we apply word sense induction to deal with ambiguous words. Finally, we cluster the disambiguated version of the ambiguous input graph into synsets. Our meta-clustering approach lets us use an efficient hard clustering algorithm to perform a fuzzy clustering of the graph. Despite its simplicity, our approach shows excellent results, outperforming five competitive state-of-the-art methods in terms of F-score on three gold standard datasets for English and Russian derived from large-scale manually constructed lexical resources.

Connected through hyperedge HE-1 (induced by source paper "place deduplication with embeddings"):
  with members:
    - "automatic synonym discovery with knowledge bases"
    - "end to end neural ad hoc ranking with kernel pooling"
    - "attention is all you need"
    - "billion scale similarity search with gpus"
    - "beyond triplet loss a deep quadruplet network for person re identification"
    - "advances in pre training distributed word representations"
    - "a deep relevance matching model for ad hoc retrieval"
    - "deep attributes driven multi camera person re identification"

Connected through hyperedge HE-2 (induced by source paper "watset local global graph clustering with applications in sense and frame induction"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "deep contextualized word representations"
    - "unsupervised semantic frame induction using triclustering"
    - "a framework for enriching lexical semantic resources with distributional semantics"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "improving hypernymy extraction with distributional semantic classes"
    - "human and machine judgements for russian semantic relatedness"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-3 (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-4 (induced by source paper "mining entity synonyms with efficient neural set generation"):
  with members:
    - "setexpan corpus based set expansion via context feature selection and rank ensemble"
    - "entity set search of scientific literature an unsupervised ranking approach"
    - "deep sets"
    - "automatic synonym discovery with knowledge bases"
    - "fighting with the sparsity of synonymy dictionaries for automatic synset induction"
    - "learning to cluster in order to transfer across domains and tasks"
    - "solving verbal comprehension questions in iq test by knowledge powered word embedding"
    - "mining the web for synonyms pmi ir versus lsa on toefl"

Connected through hyperedge HE-5 (induced by source paper "unsupervised sense aware hypernymy extraction"):
  with members:
    - "negative sampling improves hypernymy extraction based on projection learning"
    - "poincar e embeddings for learning hierarchical representations"
    - "enriching word vectors with subword information"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "autoextend extending word embeddings to embeddings for synsets and lexemes"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "an unsupervised word sense disambiguation system for under resourced languages"):
  with members:
    - "unsupervised knowledge free and interpretable word sense disambiguation"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "scikit learn machine learning in python"

Connected through hyperedge HE-7 (induced by source paper "unsupervised semantic frame induction using triclustering"):
  with members:
    - "learning frames from text with an unsupervised latent variable model"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-8 (induced by source paper "fighting with the sparsity of synonymy dictionaries for automatic synset induction"):
  with members:
    - "human and machine judgements for russian semantic relatedness"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "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)