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 "a deep reinforced model for abstractive summarization"):
  Abstract: Attentional, RNN-based encoder-decoder models for abstractive summarization have achieved good performance on short input and output sequences. For longer documents and summaries however these models often include repetitive and incoherent phrases. We introduce a neural network model with a novel intra-attention that attends over the input and continuously generated output separately, and a new training method that combines standard supervised word prediction and reinforcement learning (RL). Models trained only with supervised learning often exhibit "exposure bias" - they assume ground truth is provided at each step during training. However, when standard word prediction is combined with the global sequence prediction training of RL the resulting summaries become more readable. We evaluate this model on the CNN/Daily Mail and New York Times datasets. Our model obtains a 41.16 ROUGE-1 score on the CNN/Daily Mail dataset, an improvement over previous state-of-the-art models. Human evaluation also shows that our model produces higher quality summaries.

Includes members:
  Member "efficient summarization with read again and copy mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "summary level training of sentence rewriting for abstractive summarization")
  Member "get to the point summarization with pointer generator networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "pointing the unknown words":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "asking questions the human way scalable question answer generation from text corpus")
  Member "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "non autoregressive neural dialogue generation")
  Member "long short term memory networks for machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "time aware large kernel convolutions")
  Member "tying word vectors and word classifiers a loss framework for language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "rethinking batch normalization in transformers")
  Member "learning based single document summarization with compression and anaphoricity constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structsum incorporating latent and explicit sentence dependencies for single document summarization")
  Member "latent predictor networks for code generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi modal attention network learning for semantic source code retrieval")

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)