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 "discriminative adversarial search for abstractive summarization"):
  Abstract: We introduce a novel approach for sequence decoding, Discriminative Adversarial Search (DAS), which has the desirable properties of alleviating the effects of exposure bias without requiring external metrics. Inspired by Generative Adversarial Networks (GANs), wherein a discriminator is used to improve the generator, our method differs from GANs in that the generator parameters are not updated at training time and the discriminator is only used to drive sequence generation at inference time. #R##N#We investigate the effectiveness of the proposed approach on the task of Abstractive Summarization: the results obtained show that a naive application of DAS improves over the state-of-the-art methods, with further gains obtained via discriminator retraining. Moreover, we show how DAS can be effective for cross-domain adaptation. Finally, all results reported are obtained without additional rule-based filtering strategies, commonly used by the best performing systems available: this indicates that DAS can effectively be deployed without relying on post-hoc modifications of the generated outputs.

Includes members:
  Member "evaluating the factual consistency of abstractive text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "boosting factual correctness of abstractive summarization with knowledge graph")
  Member "answers unite unsupervised metrics for reinforced summarization models":
    also connected through other hyperedges:
      (none)
  Member "defending against neural fake news":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on contextual embeddings")
  Member "bridging the gap between training and inference for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to structure long term dependence for sequential recommendation")
  Member "better rewards yield better summaries learning to summarise without references":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning by semantic similarity makes abstractive summarization better")
  Member "pytext a seamless path from nlp research to production":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "analyzing uncertainty in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ask to learn a study on curiosity driven question generation")
  Member "bottom up abstractive summarization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "minilm deep self attention distillation for task agnostic compression of pre trained transformers")

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)