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: "planning with partially observable markov decision processes advances in exact solution method"
  Abstract: There is much interest in using partially observable Markov decision processes (POMDPs) as a formal model for planning in stochastic domains. This paper is concerned with finding optimal policies for POMDPs. We propose several improvements to incremental pruning, presently the most efficient exact algorithm for solving POMDPs.

Connected through hyperedge HE-1 (induced by source paper "policy iteration for decentralized control of markov decision processes"):
  with members:
    - "learning to cooperate via policy search"
    - "solving pomdps by searching in policy space"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "point based pomdp algorithms improved analysis and implementation"
    - "optimizing memory bounded controllers for decentralized pomdps"
    - "region based incremental pruning for pomdps"
    - "improved memory bounded dynamic programming for decentralized pomdps"
    - "maa a heuristic search algorithm for solving decentralized pomdps"

Connected through hyperedge HE-2 (induced by source paper "value function approximations for partially observable markov decision processes"):
  with members:
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "solving pomdps by searching in policy space"
    - "approximate planning for factored pomdps using belief state simplification"
    - "tractable inference for complex stochastic processes"
    - "region based approximations for planning in stochastic domains"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "decision theoretic planning structural assumptions and computational leverage"

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)