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 "regularizing output distribution of abstractive chinese social media text summarization for improved semantic consistency"):
  Abstract: Abstractive text summarization is a highly difficult problem, and the sequence-to-sequence model has shown success in improving the performance on the task. However, the generated summaries are often inconsistent with the source content in semantics. In such cases, when generating summaries, the model selects semantically unrelated words with respect to the source content as the most probable output. The problem can be attributed to heuristically constructed training data, where summaries can be unrelated to the source content, thus containing semantically unrelated words and spurious word correspondence. In this paper, we propose a regularization approach for the sequence-to-sequence model and make use of what the model has learned to regularize the learning objective to alleviate the effect of the problem. In addition, we propose a practical human evaluation method to address the problem that the existing automatic evaluation method does not evaluate the semantic consistency with the source content properly. Experimental results demonstrate the effectiveness of the proposed approach, which outperforms almost all the existing models. Especially, the proposed approach improves the semantic consistency by 4\% in terms of human evaluation.

Includes members:
  Member "a hierarchical end to end model for jointly improving text summarization and sentiment classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "unsupervised neural single document summarization of reviews via learning latent discourse structure and its ranking")
  Member "word embedding attention network generating words by querying distributed word representations for paraphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a hybrid word character model for abstractive summarization")
  Member "decoding history based adaptive control of attention for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deconvolution based global decoding for neural machine translation")
  Member "dp gan diversity promoting generative adversarial network for generating informative and diversified text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "autoregressive text generation beyond feedback loops")
  Member "get to the point summarization with pointer generator networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "incorporating copying mechanism in sequence to sequence learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "softtarget regularization an effective technique to reduce over fitting in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deepmimic mentor student unlabeled data based training")
  Member "supervised attentions for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relevance promoting language model for short text conversation")

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)