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 "deep recurrent generative decoder for abstractive text summarization"):
  Abstract: We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). #R##N#Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summarization quality. #R##N#Neural variational inference is employed to address the intractable posterior inference for the recurrent latent variables. #R##N#Abstractive summaries are generated based on both the generative latent variables and the discriminative deterministic states. #R##N#Extensive experiments on some benchmark datasets in different languages show that DRGN achieves improvements over the state-of-the-art methods.

Includes members:
  Member "distraction based neural networks for document summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "guiding extractive summarization with question answering rewards")
  Member "controlling output length in neural encoder decoders":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generating representative headlines for news stories")
  Member "incorporating copying mechanism in sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "neural summarization by extracting sentences and words":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generating representative headlines for news stories")
  Member "attsum joint learning of focusing and summarization with neural attention":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "conditional self attention for query based summarization")
  Member "language as a latent variable discrete generative models for sentence compression":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a probabilistic formulation of unsupervised text style transfer")
  Member "sequence to sequence rnns for text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")
  Member "summarunner a recurrent neural network based sequence model for extractive summarization of documents":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "prophetnet predicting future n gram for sequence to sequence pre training")

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)