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: "synergistic union of word2vec and lexicon for domain specific semantic similarity"
  Abstract: Semantic similarity measures are an important part in Natural Language Processing tasks. However Semantic similarity measures built for general use do not perform well within specific domains. Therefore in this study we introduce a domain specific semantic similarity measure that was created by the synergistic union of word2vec, a word embedding method that is used for semantic similarity calculation and lexicon based (lexical) semantic similarity methods. We prove that this proposed methodology out performs word embedding methods trained on generic corpus and methods trained on domain specific corpus but do not use lexical semantic similarity methods to augment the results. Further, we prove that text lemmatization can improve the performance of word embedding methods.

Connected through hyperedge HE-1 (induced by source paper "shift of perspective identification within legal cases"):
  with members:
    - "identifying relationships among sentences in court case transcripts using discourse relations"
    - "legal document retrieval using document vector embeddings and deep learning"
    - "fast approach to build an automatic sentiment annotator for legal domain using transfer learning"
    - "semi supervised instance population of an ontology using word vector embedding"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"
    - "gov2vec learning distributed representations of institutions and their legal text"

Connected through hyperedge HE-2 (induced by source paper "concept discovery through information extraction in restaurant domain"):
  with members:
    - "word2vec parameter learning explained"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "legal document retrieval using document vector embeddings and deep learning"):
  with members:
    - "semi supervised instance population of an ontology using word vector embedding"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"
    - "gov2vec learning distributed representations of institutions and their legal text"
    - "node2vec scalable feature learning for networks"
    - "distributed representations of sentences and documents"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-4 (induced by source paper "identifying relationships among sentences in court case transcripts using discourse relations"):
  with members:
    - "legal document retrieval using document vector embeddings and deep learning"
    - "semi supervised instance population of an ontology using word vector embedding"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"

Connected through hyperedge HE-5 (induced by source paper "semi supervised instance population of an ontology using word vector embedding"):
  with members:
    - "subject specific stream classification preprocessing algorithm for twitter data stream"
    - "sentiment analysis of citations using word2vec"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"
    - "node2vec scalable feature learning for networks"
    - "revisiting semi supervised learning with graph embeddings"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "from review to rating exploring dependency measures for text classification"):
  with members:
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "copula based kernel dependency measures"
    - "a kernel method for the two sample problem"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

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)