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: "word sense induction using word embeddings and community detection in complex networks"
  Abstract: Abstract   Word Sense Induction (WSI) is the ability to automatically induce word senses from corpora. The WSI task was first proposed to overcome the limitations of manually annotated corpus that are required in word sense disambiguation systems. Even though several works have been proposed to induce word senses, existing systems are still very limited in the sense that they make use of structured, domain-specific knowledge sources. In this paper, we devise a method that leverages recent findings in word embeddings research to generate context embeddings, which are embeddings containing information about the semantical context of a word. In order to induce senses, we modeled the set of ambiguous words as a complex network. In the generated network, two instances (nodes) are connected if the respective context embeddings are similar. Upon using well-established community detection methods to cluster the obtained context embeddings, we found that the proposed method yields excellent performance for the WSI task. Our method outperformed competing algorithms and baselines, in a completely unsupervised manner and without the need of any additional structured knowledge source.

Connected through hyperedge HE-1 (induced by source paper "semantic flow in language networks"):
  with members:
    - "connecting network science and information theory"
    - "paragraph based representation of texts a complex networks approach"
    - "representation of texts as complex networks a mesoscopic approach"
    - "word sense disambiguation a complex network approach"
    - "extractive multi document summarization using multilayer networks"
    - "labelled network subgraphs reveal stylistic subtleties in written texts"
    - "text authorship identified using the dynamics of word co occurrence networks"
    - "knowledge acquisition a complex networks approach"

Connected through hyperedge HE-2 (induced by source paper "russian word sense induction by clustering averaged word embeddings"):
  with members:
    - "size vs structure in training corpora for word embedding models araneum russicum maximum and russian national corpus"
    - "unsupervised knowledge free and interpretable word sense disambiguation"
    - "clustering comparable corpora of russian and ukrainian academic texts word embeddings and semantic fingerprints"
    - "enriching word vectors with subword information"
    - "breaking sticks and ambiguities with adaptive skip gram"

Connected through hyperedge HE-3 (induced by source paper "how much does a word weigh weighting word embeddings for word sense induction"):
  with members:
    - "unsupervised knowledge free and interpretable word sense disambiguation"
    - "breaking sticks and ambiguities with adaptive skip gram"
    - "efficient estimation of word representations in vector space"

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)