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 "learning from easy to complex adaptive multi curricula learning for neural dialogue generation"):
  Abstract: Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the learning efficiency and effects of the neural dialogue generation models. What is more, so far, there are no unified dialogue complexity measurements, and the dialogue complexity embodies multiple aspects of attributes---specificity, repetitiveness, relevance, etc. Inspired by human behaviors of learning to converse, where children learn from easy dialogues to complex ones and dynamically adjust their learning progress, in this paper, we first analyze five dialogue attributes to measure the dialogue complexity in multiple perspectives on three publicly available corpora. Then, we propose an adaptive multi-curricula learning framework to schedule a committee of the organized curricula. The framework is established upon the reinforcement learning paradigm, which automatically chooses different curricula at the evolving learning process according to the learning status of the neural dialogue generation model. Extensive experiments conducted on five state-of-the-art models demonstrate its learning efficiency and effectiveness with respect to 13 automatic evaluation metrics and human judgments.

Includes members:
  Member "improving neural conversational models with entropy based data filtering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network")
  Member "recosa detecting the relevant contexts with self attention for multi turn dialogue generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when to talk chatbot controls the timing of talking during multi turn open domain dialogue generation")
  Member "what makes a good conversation how controllable attributes affect human judgments":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "xpersona evaluating multilingual personalized chatbot")
  Member "competence based curriculum learning for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to inpaint by progressively growing the mask regions")
  Member "simple and effective curriculum pointer generator networks for reading comprehension over long narratives":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse sinkhorn attention")
  Member "dialogwae multimodal response generation with conditional wasserstein auto encoder":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "towards a human like open domain chatbot")
  Member "better conversations by modeling filtering and optimizing for coherence and diversity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "posterior gan towards informative and coherent response generation with posterior generative adversarial network")
  Member "curriculum learning by transfer learning theory and experiments with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "curriculum learning for reinforcement learning domains a framework and 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)