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 "topic augmented generator for abstractive summarization"):
  Abstract: Steady progress has been made in abstractive summarization with attention-based sequence-to-sequence learning models. In this paper, we propose a new decoder where the output summary is generated by conditioning on both the input text and the latent topics of the document. The latent topics, identified by a topic model such as LDA, reveals more global semantic information that can be used to bias the decoder to generate words. In particular, they enable the decoder to have access to additional word co-occurrence statistics captured at document corpus level. We empirically validate the advantage of the proposed approach on both the CNN/Daily Mail and the WikiHow datasets. Concretely, we attain strongly improved ROUGE scores when compared to state-of-the-art models.

Includes members:
  Member "topic guided variational autoencoders for text generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "paraphrase generation with latent bag of words")
  Member "learning to encode text as human readable summaries using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "summae zero shot abstractive text summarization using length agnostic auto encoders")
  Member "wikihow a large scale text summarization dataset":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "efficient summarization with read again and copy mechanism":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "summary level training of sentence rewriting for abstractive summarization")
  Member "generative adversarial network for abstractive text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clickbait sensational headline generation with auto tuned reinforcement learning")
  Member "get to the point summarization with pointer generator networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mast a memory augmented self supervised tracker")
  Member "a deep reinforced model for abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time aware large kernel convolutions")
  Member "abstractive text summarization using sequence to sequence rnns and beyond":
    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.LG(Machine Learning)