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: "a bayesian sampling approach to exploration in reinforcement learning"
  Abstract: We present a modular approach to reinforcement learning that uses a Bayesian representation of the uncertainty over models. The approach, BOSS (Best of Sampled Set), drives exploration by sampling multiple models from the posterior and selecting actions optimistically. It extends previous work by providing a rule for deciding when to resample and how to combine the models. We show that our algorithm achieves nearoptimal reward with high probability with a sample complexity that is low relative to the speed at which the posterior distribution converges during learning. We demonstrate that BOSS performs quite favorably compared to state-of-the-art reinforcement-learning approaches and illustrate its flexibility by pairing it with a non-parametric model that generalizes across states.

Connected through hyperedge HE-1 (induced by source paper "worst case regret bounds for exploration via randomized value functions"):
  with members:
    - "randomised bayesian least squares policy iteration"
    - "exploration by random network distillation"
    - "randomized value functions via multiplicative normalizing flows"
    - "is q learning provably efficient"
    - "efficient exploration through bayesian deep q networks"
    - "noisy networks for exploration"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "deep exploration via bootstrapped dqn"

Connected through hyperedge HE-2 (induced by source paper "bayesian reinforcement learning via deep sparse sampling"):
  with members:
    - "multi step greedy and approximate real time dynamic programming"
    - "bayesian reinforcement learning a survey"
    - "learning to optimize via posterior sampling"
    - "monte carlo utility estimates for bayesian reinforcement learning"
    - "near optimal brl using optimistic local transitions"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "robust bayesian reinforcement learning through tight lower bounds"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "learning awareness models"):
  with members:
    - "distributed prioritized experience replay"
    - "curiosity driven reinforcement learning with homeostatic regulation"
    - "parameter space noise for exploration"
    - "learning to represent haptic feedback for partially observable tasks"
    - "ex2 exploration with exemplar models for deep reinforcement learning"
    - "the consciousness prior"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "preparing for the unknown learning a universal policy with online system identification"

Connected through hyperedge HE-6 (induced by source paper "controlled random search improves sample mining and hyper parameter optimization"):
  with members:
    - "interpretable and pedagogical examples"
    - "sampling matters in deep embedding learning"
    - "critical hyper parameters no random no cry"
    - "importance sampling for minibatches"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "posterior sampling for reinforcement learning worst case regret bounds"):
  with members:
    - "bayesian optimal control of smoothly parameterized systems the lazy posterior sampling algorithm"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "further optimal regret bounds for thompson sampling"
    - "thompson sampling for contextual bandits with linear payoffs"
    - "regal a regularization based algorithm for reinforcement learning in weakly communicating mdps"
    - "pac bayesian inequalities for martingales"
    - "analysis of thompson sampling for the multi armed bandit problem"

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.LG(Machine Learning)