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 with double uncertain value networks"
  Abstract: This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action. We identify two sources of uncertainty that are relevant for exploration. The first originates from limited data (parametric uncertainty), while the second originates from the distribution of the returns (return uncertainty). We identify methods to learn these distributions with deep neural networks, where we estimate parametric uncertainty with Bayesian drop-out, while return uncertainty is propagated through the Bellman equation as a Gaussian distribution. Then, we identify that both can be jointly estimated in one network, which we call the Double Uncertain Value Network. The policy is directly derived from the learned distributions based on Thompson sampling. Experimental results show that both types of uncertainty may vastly improve learning in domains with a strong exploration challenge.

Connected through hyperedge HE-1 (induced by source paper "uncertainty sensitive learning and planning with ensembles"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "model based reinforcement learning for atari"
    - "adaptive online planning for continual lifelong learning"
    - "an investigation of model free planning"
    - "striving for simplicity in off policy deep reinforcement learning"
    - "single agent policy tree search with guarantees"
    - "self imitation learning"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-2 (induced by source paper "deep intrinsically motivated continuous actor critic for efficient robotic visuomotor skill learning"):
  with members:
    - "intrinsic motivation and automatic curricula via asymmetric self play"
    - "curiosity driven exploration by self supervised prediction"
    - "reinforcement learning with unsupervised auxiliary tasks"
    - "asynchronous methods for deep reinforcement learning"
    - "hierarchical deep reinforcement learning integrating temporal abstraction and intrinsic motivation"
    - "vime variational information maximizing exploration"
    - "prioritized experience replay"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "successor uncertainties exploration and uncertainty in temporal difference learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "count based exploration with the successor representation"
    - "randomized value functions via multiplicative normalizing flows"
    - "information directed exploration for deep reinforcement learning"
    - "efficient exploration through bayesian deep q networks"
    - "parameter space noise for exploration"
    - "shallow updates for deep reinforcement learning"
    - "eigenoption discovery through the deep successor representation"

Connected through hyperedge HE-4 (induced by source paper "information directed exploration for deep reinforcement learning"):
  with members:
    - "exploration by distributional reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "efficient exploration through bayesian deep q networks"
    - "the potential of the return distribution for exploration in rl"
    - "parameter space noise for exploration"
    - "the uncertainty bellman equation and exploration"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"

Connected through hyperedge HE-5 (induced by source paper "implicit quantile networks for distributional reinforcement learning"):
  with members:
    - "how should a robot assess risk towards an axiomatic theory of risk in robotics"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"
    - "particle value functions"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "prioritized experience replay"
    - "dueling network architectures for deep reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "the potential of the return distribution for exploration in rl"):
  with members:
    - "exploration by distributional reinforcement learning"
    - "bayesian policy gradients via alpha divergence dropout inference"
    - "a distributional perspective on reinforcement learning"
    - "variational deep q network"
    - "a tutorial on thompson sampling"
    - "deep exploration via bootstrapped dqn"
    - "parametric return density estimation for reinforcement learning"
    - "bayesian inference in monte carlo tree search"

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)