human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "shift of perspective identification within legal cases"):
  Abstract: Arguments, counter-arguments, facts, and evidence obtained via documents related to previous court cases are of essential need for legal professionals. Therefore, the process of automatic information extraction from documents containing legal opinions related to court cases can be considered to be of significant importance. This study is focused on the identification of sentences in legal opinion texts which convey different perspectives on a certain topic or entity. We combined several approaches based on semantic analysis, open information extraction, and sentiment analysis to achieve our objective. Then, our methodology was evaluated with the help of human judges. The outcomes of the evaluation demonstrate that our system is successful in detecting situations where two sentences deliver different opinions on the same topic or entity. The proposed methodology can be used to facilitate other information extraction tasks related to the legal domain. One such task is the automated detection of counter arguments for a given argument. Another is the identification of opponent parties in a court case.

Includes members:
  Member "identifying relationships among sentences in court case transcripts using discourse relations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast approach to build an automatic sentiment annotator for legal domain using transfer learning")
  Member "legal document retrieval using document vector embeddings and deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "identifying relationships among sentences in court case transcripts using discourse relations")
  Member "fast approach to build an automatic sentiment annotator for legal domain using transfer learning":
    also connected through other hyperedges:
      (none)
  Member "semi supervised instance population of an ontology using word vector embedding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "legal document retrieval using document vector embeddings and deep learning")
  Member "synergistic union of word2vec and lexicon for domain specific semantic similarity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "concept discovery through information extraction in restaurant domain")
  Member "deriving a representative vector for ontology classes with instance word vector embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "legal document retrieval using document vector embeddings and deep learning")
  Member "gov2vec learning distributed representations of institutions and their legal text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "an external knowledge enhanced multi label charge prediction approach with label number learning")

Question: Please tell me which class the source paper 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)