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 "guided policy exploration for markov decision processes using an uncertainty based value of information criterion"):
  Abstract: Reinforcement learning in environments with many action–state pairs is challenging. The issue is the number of episodes needed to thoroughly search the policy space. Most conventional heuristics address this search problem in a stochastic manner. This can leave large portions of the policy space unvisited during the early training stages. In this paper, we propose an uncertainty-based, information-theoretic approach for performing guided stochastic searches that more effectively cover the policy space. Our approach is based on the value of information, a criterion that provides the optimal tradeoff between expected costs and the granularity of the search process. The value of information yields a stochastic routine for choosing actions during learning that can explore the policy space in a coarse to fine manner. We augment this criterion with a state-transition uncertainty factor, which guides the search process into previously unexplored regions of the policy space. We evaluate the uncertainty-based value-of-information policies on the games  Centipede  and  Crossy Road . Our results indicate that our approach yields better performing policies in fewer episodes than stochastic-based exploration strategies. We show that the training rate for our approach can be further improved by using the policy cross entropy to guide our criterion’s hyperparameter selection.

Includes members:
  Member "analysis of agent expertise in ms pac man using value of information based policies":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reduction of markov chains using a value of information based approach")
  Member "an analysis of the value of information when exploring stochastic discrete multi armed bandits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of agent expertise in ms pac man using value of information based policies")
  Member "partitioning relational matrices of similarities or dissimilarities using the value of information":
    also connected through other hyperedges:
      (none)
  Member "model based bayesian exploration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "benchmarking model based reinforcement learning")
  Member "incremental model based learners with formal learning time guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "temple learning template of transitions for sample efficient multi task rl")
  Member "model based bayesian reinforcement learning in large structured domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "expanding the active inference landscape more intrinsic motivations in the perception action loop")
  Member "reinforcement learning a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online fast adaptation and knowledge accumulation a new approach to continual learning")

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)