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 "hierarchical text generation and planning for strategic dialogue"):
  Abstract: End-to-end models for strategic dialogue are challenging to train, because linguistic and strategic aspects are entangled in latent state vectors. We introduce an approach to generating latent representations of dialogue moves, by inducing sentence representations to maximize the likelihood of subsequent sentences and actions. The effect is to decouple much of the semantics of the utterance from its linguistic realisation. We then use these latent sentence representations for hierarchical language generation, planning and reinforcement learning. Experiments show that using our message representations increases the reward achieved by the model, improves the effectiveness of long-term planning using rollouts, and allows self-play reinforcement learning to improve decision making without diverging from human language. Our hierarchical latent-variable model outperforms previous work both linguistically and strategically.

Includes members:
  Member "discrete autoencoders for sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "model based reinforcement learning for atari")
  Member "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "crwiz a framework for crowdsourcing real time wizard of oz dialogues")
  Member "supervised learning of universal sentence representations from natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on contextual embeddings")
  Member "latent intention dialogue models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sequential latent knowledge selection for knowledge grounded dialogue")
  Member "learning cooperative visual dialog agents with deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grad cam visual explanations from deep networks via gradient based localization")
  Member "latent variable dialogue models and their diversity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modelling latent skills for multitask language generation")
  Member "deal or no deal end to end learning for negotiation dialogues":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "recent advances and challenges in task oriented dialog system")
  Member "neural discrete representation learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning representations by predicting bags of visual words")

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)