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 "planning with information processing constraints and model uncertainty in markov decision processes"):
  Abstract: Information-theoretic principles for learning and acting have been proposed to solve particular classes of Markov Decision Problems. Mathematically, such approaches are governed by a variational free energy principle and allow solving MDP planning problems with information-processing constraints expressed in terms of a Kullback-Leibler divergence with respect to a reference distribution. Here we consider a generalization of such MDP planners by taking model uncertainty into account. As model uncertainty can also be formalized as an information-processing constraint, we can derive a unified solution from a single generalized variational principle. We provide a generalized value iteration scheme together with a convergence proof. As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning. We demonstrate the benefits of this approach in a grid world simulation.

Includes members:
  Member "taming the noise in reinforcement learning via soft updates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "addressing value estimation errors in reinforcement learning with a state action return distribution function")
  Member "risk sensitive and robust decision making a cvar optimization approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wasserstein distributionally robust motion control for collision avoidance using conditional value at risk")
  Member "g learning taming the noise in reinforcement learning via soft updates":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "information asymmetry in kl regularized rl")
  Member "generalized thompson sampling for sequential decision making and causal inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "causal induction from visual observations for goal directed tasks")
  Member "risk sensitive reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fully bayesian recurrent neural networks for safe reinforcement learning")
  Member "efficient bayes adaptive reinforcement learning using sample based search":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "making sense of reinforcement learning and probabilistic inference")
  Member "risk sensitive path integral control":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "meta learning of sequential strategies")
  Member "a bayesian rule for adaptive control based on causal interventions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "stacked thompson bandits")

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.AI(Artificial Intelligence)