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: "analysis of agent expertise in ms pac man using value of information based policies"
  Abstract: Conventional reinforcement-learning methods for Markov decision processes rely on weakly guided, stochastic searches to drive the learning process. It can therefore be difficult to predict what agent behaviors might emerge. In this paper, we consider an information-theoretic cost function for performing constrained stochastic searches that promote the formation of risk-averse to risk-favoring behaviors. This cost function is the value of information, which provides the optimal tradeoff between the expected return of a policy and the policy's complexity; policy complexity is measured by number of bits and controlled by a single hyperparameter on the cost function. As the policy complexity is reduced, the agents will increasingly eschew risky actions. This reduces the potential for high accrued rewards. As the policy complexity increases, the agents will take actions, regardless of the risk, that can raise the long-term rewards. The obtainable reward depends on a single, tunable hyperparameter that regulates the degree of policy complexity. We evaluate the performance of value-of-information-based policies on a stochastic version of  Ms. Pac-Man . A major component of this paper is the demonstration that ranges of policy complexity values yield different game-play styles and explaining why this occurs. We also show that our reinforcement-learning search mechanism is more efficient than the others we utilize. This result implies that the value-of-information theory is appropriate for framing the exploitation–exploration tradeoff in reinforcement learning.

Connected through hyperedge HE-1 (induced by source paper "reduction of markov chains using a value of information based approach"):
  with members:
    - "guided policy exploration for markov decision processes using an uncertainty based value of information criterion"
    - "an analysis of the value of information when exploring stochastic discrete multi armed bandits"
    - "optimal kullback leibler aggregation via information bottleneck"
    - "a metric between probability distributions on finite sets of different cardinalities and applications to order reduction"

Connected through hyperedge HE-2 (induced by source paper "guided policy exploration for markov decision processes using an uncertainty based value of information criterion"):
  with members:
    - "an analysis of the value of information when exploring stochastic discrete multi armed bandits"
    - "partitioning relational matrices of similarities or dissimilarities using the value of information"
    - "model based bayesian exploration"
    - "incremental model based learners with formal learning time guarantees"
    - "model based bayesian reinforcement learning in large structured domains"
    - "reinforcement learning a survey"

Connected through hyperedge HE-3 (induced by source paper "an analysis of the value of information when exploring stochastic discrete multi armed bandits"):
  with members:
    - "robust interactive learning"
    - "reinforcement learning a survey"

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)