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: "model based bayesian reinforcement learning in large structured domains"
  Abstract: Model-based Bayesian reinforcement learning has generated significant interest in the AI community as it provides an elegant solution to the optimal exploration-exploitation tradeoff in classical reinforcement learning. Unfortunately, the applicability of this type of approach has been limited to small domains due to the high complexity of reasoning about the joint posterior over model parameters. In this paper, we consider the use of factored representations combined with online planning techniques, to improve scalability of these methods. The main contribution of this paper is a Bayesian framework for learning the structure and parameters of a dynamical system, while also simultaneously planning a (near-)optimal sequence of actions.

Connected through hyperedge HE-1 (induced by source paper "expanding the active inference landscape more intrinsic motivations in the perception action loop"):
  with members:
    - "new and surprising ways to be mean adversarial npcs with coupled empowerment minimisation"
    - "accelerating empowerment computation with uct tree search"
    - "universal reinforcement learning algorithms survey and experiments"
    - "minimum information lqg control part ii retentive controllers"
    - "nonparametric general reinforcement learning"
    - "variational intrinsic control"
    - "the umwelt of an embodied agent a measure theoretic definition"
    - "generalized thompson sampling for sequential decision making and causal inference"

Connected through hyperedge HE-2 (induced by source paper "bayesian reinforcement learning in factored pomdps"):
  with members:
    - "deep variational reinforcement learning for pomdps"
    - "learning in pomdps with monte carlo tree search"
    - "efficient exploration through bayesian deep q networks"
    - "on improving deep reinforcement learning for pomdps"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q learning"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "approximate planning for factored pomdps using belief state simplification"

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"
    - "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"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (induced by source paper "bayesian reinforcement learning a survey"):
  with members:
    - "benchmarking for bayesian reinforcement learning"
    - "on the prior sensitivity of thompson sampling"
    - "an information theoretic analysis of thompson sampling"
    - "learning to optimize via posterior sampling"
    - "a greedy approximation of bayesian reinforcement learning with probably optimistic transition model"
    - "model based bayesian exploration"
    - "further optimal regret bounds for thompson sampling"
    - "near optimal brl using optimistic local transitions"

Connected through hyperedge HE-5 (induced by source paper "better optimism by bayes adaptive planning with rich models"):
  with members:
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "safe exploration in markov decision processes"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-6 (induced by source paper "monte carlo bayesian reinforcement learning"):
  with members:
    - "model based bayesian exploration"
    - "pegasus a policy search method for large mdps and pomdps"
    - "point based pomdp algorithms improved analysis and implementation"
    - "a bayesian sampling approach to exploration in reinforcement learning"
    - "on some winning strategies for the iterated prisoner s dilemma or mr nice guy and the cosa nostra"

Connected through hyperedge HE-7 (induced by source paper "feature reinforcement learning part i unstructured mdps"):
  with members:
    - "model based bayesian exploration"
    - "feature dynamic bayesian networks"
    - "online planning algorithms for pomdps"
    - "feature markov decision processes"
    - "universal algorithmic intelligence a mathematical top down approach"
    - "universal intelligence a definition of machine intelligence"
    - "universal learning of repeated matrix games"
    - "optimal ordered problem solver"

Connected through hyperedge HE-8 (induced by source paper "closing the learning planning loop with predictive state representations"):
  with members:
    - "predictive state representations a new theory for modeling dynamical systems"
    - "planning in pomdps using multiplicity automata"
    - "reduced rank hidden markov models"
    - "a spectral algorithm for learning hidden markov models"
    - "perseus randomized point based value iteration for pomdps"

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)