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: "an analysis of the value of information when exploring stochastic discrete multi armed bandits"
  Abstract: In this paper, we propose an information-theoretic exploration strategy for stochastic, discrete multi-armed bandits that achieves optimal regret. Our strategy is based on the value of information criterion. This criterion measures the trade-off between policy information and obtainable rewards. High amounts of policy information are associated with exploration-dominant searches of the space and yield high rewards. Low amounts of policy information favor the exploitation of existing knowledge. Information, in this criterion, is quantified by a parameter that can be varied during search. We demonstrate that a simulated-annealing-like update of this parameter, with a sufficiently fast cooling schedule, leads to a regret that is logarithmic with respect to the number of arm pulls.

Connected through hyperedge HE-1 (induced by source paper "analysis of agent expertise in ms pac man using value of information based policies"):
  with members:
    - "guided policy exploration for markov decision processes using an uncertainty based value of information criterion"
    - "strategic attentive writer for learning macro actions"
    - "learning values across many orders of magnitude"
    - "action conditional video prediction using deep networks in atari games"
    - "reinforcement learning a survey"

Connected through hyperedge HE-2 (induced by source paper "reduction of markov chains using a value of information based approach"):
  with members:
    - "analysis of agent expertise in ms pac man using value of information based policies"
    - "guided policy exploration for markov decision processes using an uncertainty based value of information criterion"
    - "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-3 (induced by source paper "guided policy exploration for markov decision processes using an uncertainty based value of information criterion"):
  with members:
    - "analysis of agent expertise in ms pac man using value of information based policies"
    - "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"

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.AI(Artificial Intelligence)