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: "deriving a representative vector for ontology classes with instance word vector embeddings"
  Abstract: Selecting a representative vector for a set of vectors is a very common requirement in many algorithmic tasks. Traditionally, the mean or median vector is selected. Ontology classes are sets of homogeneous instance objects that can be converted to a vector space by word vector embeddings. This study proposes a methodology to derive a representative vector for ontology classes whose instances were converted to the vector space. We start by deriving five candidate vectors which are then used to train a machine learning model that would calculate a representative vector for the class. We show that our methodology out-performs the traditional mean and median vector representations.

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"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "gov2vec learning distributed representations of institutions and their legal text"

Connected through hyperedge HE-2 (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"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "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-3 (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"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"

Connected through hyperedge HE-4 (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"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "sentiment analysis of citations using word2vec"
    - "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-5 (induced by source paper "synergistic union of word2vec and lexicon for domain specific semantic similarity"):
  with members:
    - "semi supervised instance population of an ontology using word vector embedding"
    - "gov2vec learning distributed representations of institutions and their legal text"
    - "word2vec explained deriving mikolov et al s negative sampling word embedding method"
    - "efficient estimation of word representations in vector space"
    - "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)