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: "efficient exploration for dialog policy learning with deep bbq networks replay buffer spiking"
  Abstract: When rewards are sparse and action spaces large, Q-learning with $\epsilon$-greedy exploration can be inefficient. This poses problems for otherwise promising applications such as task-oriented dialogue systems, where the primary reward signal, indicating successful completion of a task, requires a complex sequence of appropriate actions. Under these circumstances, a randomly exploring agent might never stumble upon a successful outcome in reasonable time. We present two techniques that significantly improve the efficiency of exploration for deep Q-learning agents in dialogue systems. First, we introduce an exploration technique based on Thompson sampling, drawing Monte Carlo samples from a Bayes-by-backprop neural network, demonstrating marked improvement over common approaches such as $\epsilon$-greedy and Boltzmann exploration. Second, we show that spiking the replay buffer with experiences from a small number of successful episodes, as are easy to harvest for dialogue tasks, can make Q-learning feasible when it might otherwise fail.

Connected through hyperedge HE-1 (induced by source paper "agentgraph towards universal dialogue management with structured deep reinforcement learning"):
  with members:
    - "towards universal dialogue state tracking"
    - "sample efficient deep reinforcement learning for dialogue systems with large action spaces"
    - "feudal reinforcement learning for dialogue management in large domains"
    - "large scale multi domain belief tracking with knowledge sharing"
    - "rainbow combining improvements in deep reinforcement learning"
    - "non local neural networks"
    - "sample efficient actor critic reinforcement learning with supervised data for dialogue management"
    - "composite task completion dialogue policy learning via hierarchical deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "budgeted policy learning for task oriented dialogue systems"):
  with members:
    - "microsoft dialogue challenge building end to end task completion dialogue systems"
    - "dialogue learning with human teaching and feedback in end to end trainable task oriented dialogue systems"
    - "switch based active deep dyna q efficient adaptive planning for task completion dialogue policy learning"
    - "integrating planning for task completion dialogue policy learning"
    - "neural approaches to conversational ai"
    - "discriminative deep dyna q robust planning for dialogue policy learning"
    - "iterative policy learning in end to end trainable task oriented neural dialog models"
    - "a tutorial on thompson sampling"

Connected through hyperedge HE-3 (induced by source paper "a comprehensive guide to bayesian convolutional neural network with variational inference"):
  with members:
    - "uncertainty estimations by softplus normalization in bayesian convolutional neural networks with variational inference"
    - "exploring sparsity in recurrent neural networks"
    - "the power of sparsity in convolutional neural networks"
    - "bayesian recurrent neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "curiosity driven exploration in deep reinforcement learning via bayesian neural networks"
    - "stochastic backpropagation through mixture density distributions"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "reinforced cross modal matching and self supervised imitation learning for vision language navigation"):
  with members:
    - "tactical rewind self correction via backtracking in vision and language navigation"
    - "self monitoring navigation agent via auxiliary progress estimation"
    - "the regretful agent heuristic aided navigation through progress estimation"
    - "man moment alignment network for natural language moment retrieval via iterative graph adjustment"
    - "turbo learning for captionbot and drawingbot"
    - "watch listen and describe globally and locally aligned cross modal attentions for video captioning"
    - "gibson env real world perception for embodied agents"
    - "self imitation learning"

Connected through hyperedge HE-5 (induced by source paper "efficient exploration through bayesian deep q networks"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "deep reinforcement learning that matters"
    - "equivalence between policy gradients and soft q learning"
    - "shallow updates for deep reinforcement learning"
    - "count based exploration with neural density models"
    - "noisy networks for exploration"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-6 (induced by source paper "learn what not to learn action elimination with deep reinforcement learning"):
  with members:
    - "using reinforcement learning to learn how to play text based games"
    - "textworld a learning environment for text based games"
    - "counting to explore and generalize in text based games"
    - "efficient exploration through bayesian deep q networks"
    - "shallow updates for deep reinforcement learning"
    - "end to end task completion neural dialogue systems"
    - "a deep reinforcement learning chatbot"
    - "combating deep reinforcement learning s sisyphean curse with intrinsic fear"

Connected through hyperedge HE-7 (induced by source paper "surprising negative results for generative adversarial tree search"):
  with members:
    - "world models"
    - "spectral normalization for generative adversarial networks"
    - "the effect of planning shape on dyna style planning in high dimensional state spaces"
    - "efficient exploration through bayesian deep q networks"
    - "value prediction network"
    - "count based exploration with neural density models"
    - "improved training of wasserstein gans"
    - "noisy networks for exploration"

Connected through hyperedge HE-8 (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"

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.LG(Machine Learning)