human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "an application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email"
  Abstract: This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (Walker et al., 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named elvis (EmaiL Voice Interactive System), that supports access to email over the phone. We conduct a set of experiments for training an optimal dialogue strategy on a corpus of 219 dialogues in which human users interact with elvis over the phone. We then test that strategy on a corpus of 18 dialogues. We show that elvis can learn to optimize its strategy selection for agent initiative, for reading messages, and for summarizing email folders.

Connected through hyperedge HE-1 (induced by source paper "improving interaction quality estimation with bilstms and the impact on dialogue policy learning"):
  with members:
    - "attention is all you need"
    - "on line active reward learning for policy optimisation in spoken dialogue systems"
    - "learning from real users rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems"

Connected through hyperedge HE-2 (induced by source paper "rethinking action spaces for reinforcement learning in end to end dialog agents with latent variable models"):
  with members:
    - "towards universal dialogue state tracking"
    - "unsupervised discrete sentence representation learning for interpretable neural dialog generation"
    - "decoupling strategy and generation in negotiation dialogues"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "emergence of grounded compositional language in multi agent populations"
    - "hierarchical text generation and planning for strategic dialogue"

Connected through hyperedge HE-3 (induced by source paper "modeling multi action policy for task oriented dialogues"):
  with members:
    - "user modeling for task oriented dialogues"
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "recurrently controlled recurrent networks"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "incorporating copying mechanism in sequence to sequence learning"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for inquiry dialog policies with logical formula embeddings"):
  with members:
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "playing atari with deep reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "combining search with structured data to create a more engaging user experience in open domain dialogue"):
  with members:
    - "m2d monolog to dialog generation for conversational story telling"
    - "automatically training a problematic dialogue predictor for a spoken dialogue system"

Connected through hyperedge HE-6 (induced by source paper "reward balancing for statistical spoken dialogue systems using multi objective reinforcement learning"):
  with members:
    - "on line active reward learning for policy optimisation in spoken dialogue systems"

Connected through hyperedge HE-7 (induced by source paper "deep reinforcement learning for dialogue generation"):
  with members:
    - "a persona based neural conversation model"
    - "a network based end to end trainable task oriented dialogue system"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "lstm based conversation models"
    - "continuously learning neural dialogue management"
    - "incorporating loose structured knowledge into conversation modeling via recall gate lstm"
    - "deltableu a discriminative metric for generation tasks with intrinsically diverse targets"
    - "building end to end dialogue systems using generative hierarchical neural network models"

Connected through hyperedge HE-8 (induced by source paper "bbq networks efficient exploration in deep reinforcement learning for task oriented dialogue systems"):
  with members:
    - "a network based end to end trainable task oriented dialogue system"
    - "simpleds a simple deep reinforcement learning dialogue system"
    - "curiosity driven exploration in deep reinforcement learning via bayesian neural networks"
    - "deep exploration via bootstrapped dqn"
    - "overcoming catastrophic forgetting in neural networks"
    - "end to end lstm based dialog control optimized with supervised and reinforcement learning"
    - "a user simulator for task completion dialogues"
    - "policy networks with two stage training for dialogue systems"

Question: Please tell me which class the center node 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.AI(Artificial Intelligence)