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: "sample efficient deep reinforcement learning for dialog control"
  Abstract: Representing a dialog policy as a recurrent neural network (RNN) is attractive because it handles partial observability, infers a latent representation of state, and can be optimized with supervised learning (SL) or reinforcement learning (RL). For RL, a policy gradient approach is natural, but is sample inefficient. In this paper, we present 3 methods for reducing the number of dialogs required to optimize an RNN-based dialog policy with RL. The key idea is to maintain a second RNN which predicts the value of the current policy, and to apply experience replay to both networks. On two tasks, these methods reduce the number of dialogs/episodes required by about a third, vs. standard policy gradient methods.

Connected through hyperedge HE-1 (induced by source paper "generalized off policy actor critic"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "online off policy prediction"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-2 (induced by source paper "efficient dialog policy learning via positive memory retention"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "learning goal oriented visual dialog via tempered policy gradient"
    - "evaluating visual conversational agents via cooperative human ai games"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "learning cooperative visual dialog agents with deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "visual reference resolution using attention memory for visual dialog"

Connected through hyperedge HE-3 (induced by source paper "sample efficient deep reinforcement learning for dialogue systems with large action spaces"):
  with members:
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "latent intention dialogue models"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "end to end task completion neural dialogue systems"
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "investigation of language understanding impact for reinforcement learning based dialogue systems"
    - "towards end to end learning for dialog state tracking and management using deep reinforcement learning"
    - "sample efficient actor critic with experience replay"

Connected through hyperedge HE-4 (induced by source paper "sample efficient actor critic reinforcement learning with supervised data for dialogue management"):
  with members:
    - "hybrid code networks practical and efficient end to end dialog control with supervised and reinforcement learning"
    - "deep q learning from demonstrations"
    - "deep reinforcement learning for dialogue generation"
    - "continuously learning neural dialogue management"
    - "policy networks with two stage training for dialogue systems"
    - "neural belief tracker data driven dialogue state tracking"
    - "end to end joint learning of natural language understanding and dialogue manager"
    - "learning end to end goal oriented dialog"

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)