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 "point less more abstractive summarization with pointer generator networks"):
  Abstract: The Pointer-Generator architecture has shown to be a big improvement for abstractive summarization seq2seq models. However, the summaries produced by this model are largely extractive as over 30% of the generated sentences are copied from the source text. This work proposes a multihead attention mechanism, pointer dropout, and two new loss functions to promote more abstractive summaries while maintaining similar ROUGE scores. Both the multihead attention and dropout do not improve N-gram novelty, however, the dropout acts as a regularizer which improves the ROUGE score. The new loss function achieves significantly higher novel N-grams and sentences, at the cost of a slightly lower ROUGE score.

Includes members:
  Member "improving abstraction in text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "avgout a simple output probability measure to eliminate dull responses")
  Member "query focused abstractive summarization incorporating query relevance multi document coverage and summary length constraints into seq2seq models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating representative headlines for news stories")
  Member "structure infused copy mechanisms for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "robust neural abstractive summarization systems and evaluation against adversarial information":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an entity driven framework for abstractive summarization")
  Member "abstractive summarization using attentive neural techniques":
    also connected through other hyperedges:
      (none)
  Member "controlling decoding for more abstractive summaries with copy based networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on extractive and abstractive neural document summarization with transformer language models")
  Member "deep recurrent generative decoder for abstractive text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")
  Member "text summarization techniques a brief survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "human like time series summaries via trend utility estimation")

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)