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 "what is the essence of a claim cross domain claim identification"):
  Abstract: Argument mining has become a popular research area in NLP. It typically includes the identification of argumentative components, e.g. claims, as the central component of an argument. We perform a qualitative analysis across six different datasets and show that these appear to conceptualize claims quite differently. To learn about the consequences of such different conceptualizations of claim for practical applications, we carried out extensive experiments using state-of-the-art feature-rich and deep learning systems, to identify claims in a cross-domain fashion. While the divergent perception of claims in different datasets is indeed harmful to cross-domain classification, we show that there are shared properties on the lexical level as well as system configurations that can help to overcome these gaps.

Includes members:
  Member "argumentation mining in user generated web discourse":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "neural end to end learning for computational argumentation mining":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "parsing argumentation structures in persuasive essays":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "argulens anatomy of community opinions on usability issues using argumentation models")
  Member "dependency sensitive convolutional neural networks for modeling sentences and documents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "text classification with lexicon from preattention mechanism")
  Member "mgnc cnn a simple approach to exploiting multiple word embeddings for sentence classification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "benchmark performance of machine and deep learning based methodologies for urdu text document classification")
  Member "using summarization to discover argument facets in online idealogical dialog":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "samsum corpus a human annotated dialogue dataset for abstractive summarization")
  Member "unsupervised domain adaptation with feature embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised domain adaptation of contextualized embeddings for sequence labeling")
  Member "a pdtb styled end to end discourse parser":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discourse level factors for sentence deletion in text simplification")

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)