human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "estimating risk and uncertainty in deep reinforcement learning"):
  Abstract: We propose a method for disentangling epistemic and aleatoric uncertainties in deep reinforcement learning. Aleatoric uncertainty, or risk, which arises from inherently stochastic environments or agents, must be accounted for in the design of risk-sensitive algorithms. Epistemic uncertainty, which stems from limited data, is important both for risk-sensitivity and for efficient exploration. Our method combines elements from distributional reinforcement learning and approximate Bayesian inference techniques with neural networks, allowing us to disentangle both types of uncertainty on the expected return of a policy. Specifically, the learned return distribution provides the aleatoric uncertainty, and the Bayesian posterior yields the epistemic uncertainty. Although our approach in principle requires a large number of samples from the Bayesian posterior to estimate the epistemic uncertainty, we show that two networks already yield a useful approximation. We perform experiments that illustrate our method and some applications.

Includes members:
  Member "model predictive policy learning with uncertainty regularization for driving in dense traffic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "behavior planning for connected autonomous vehicles using feedback deep reinforcement learning")
  Member "exploration by random network distillation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transformation based adversarial video prediction on large scale data")
  Member "measuring and characterizing generalization in deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust visual domain randomization for reinforcement learning")
  Member "information directed exploration for deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sample efficient model free reinforcement learning with off policy critics")
  Member "mitigating planner overfitting in model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "personalized heartsteps a reinforcement learning algorithm for optimizing physical activity")
  Member "safe reinforcement learning with model uncertainty estimates":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "visual based autonomous driving deployment from a stochastic and uncertainty aware perspective")
  Member "exploration by distributional reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "inferential induction joint bayesian estimation of mdps and value functions")
  Member "implicit quantile networks for distributional reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sample based distributional policy gradient")

Question: Please tell me which class the source paper 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)