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: "semi supervised instance population of an ontology using word vector embedding"
  Abstract: In many modern-day systems such as information extraction and knowledge management agents, ontologies play a vital role in maintaining the concept hierarchies of the selected domain. However, ontology population has become a problematic process due to its nature of heavy coupling with manual human intervention. With the use of word embeddings in the field of natural language processing, it became a popular topic due to its ability to cope up with semantic sensitivity. Hence, in this study we propose a novel way of semi-supervised ontology population through word embeddings as the basis. We built several models including traditional benchmark models and new types of models which are based on word embeddings. Finally, we ensemble them together to come up with a synergistic model with better accuracy. We demonstrate that our ensemble model can outperform the individual models.

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"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "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 "legal document retrieval using document vector embeddings and deep learning"):
  with members:
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "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-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"
    - "synergistic union of word2vec and lexicon for domain specific semantic similarity"
    - "deriving a representative vector for ontology classes with instance word vector embeddings"

Connected through hyperedge HE-4 (induced by source paper "synergistic union of word2vec and lexicon for domain specific semantic similarity"):
  with members:
    - "deriving a representative vector for ontology classes with instance word vector embeddings"
    - "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)