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 "neural keyphrase generation via reinforcement learning with adaptive rewards"):
  Abstract: Generating keyphrases that summarize the main points of a document is a fundamental task in natural language processing. Although existing generative models are capable of predicting multiple keyphrases for an input document as well as determining the number of keyphrases to generate, they still suffer from the problem of generating too few keyphrases. To address this problem, we propose a reinforcement learning (RL) approach for keyphrase generation, with an adaptive reward function that encourages a model to generate both sufficient and accurate keyphrases. Furthermore, we introduce a new evaluation method that incorporates name variations of the ground-truth keyphrases using the Wikipedia knowledge base. Thus, our evaluation method can more robustly evaluate the quality of predicted keyphrases. Extensive experiments on five real-world datasets of different scales demonstrate that our RL approach consistently and significantly improves the performance of the state-of-the-art generative models with both conventional and new evaluation methods.

Includes members:
  Member "an integrated approach for keyphrase generation via exploring the power of retrieval and extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "keyphrase generation a multi aspect survey")
  Member "microblog hashtag generation via encoding conversation contexts":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "topic aware neural keyphrase generation for social media language")
  Member "keyphrase generation with correlation constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "c textgen conditional text generation for harmonious human machine interaction")
  Member "semi supervised learning for neural keyphrase generation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "large scale evaluation of keyphrase extraction models")
  Member "gaan gated attention networks for learning on large and spatiotemporal graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "multi head attention with disagreement regularization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "assessing the ability of self attention networks to learn word order")
  Member "generating diverse numbers of diverse keyphrases":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "keyphrase generation a multi aspect survey")

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)