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: "a model approximation scheme for planning in partially observable stochastic domains"
  Abstract: Partially observable Markov decision processes (POMDPs) are a natural model for planning problems where effects of actions are nondeterministic and the state of the world is not completely observable. It is difficult to solve POMDPs exactly. This paper proposes a new approximation scheme. The basic idea is to transform a POMDP into another one where additional information is provided by an oracle. The oracle informs the planning agent that the current state of the world is in a certain region. The transformed POMDP is consequently said to be region observable. It is easier to solve than the original POMDP. We propose to solve the transformed POMDP and use its optimal policy to construct an approximate policy for the original POMDP. By controlling the amount of additional information that the oracle provides, it is possible to find a proper tradeoff between computational time and approximation quality. In terms of algorithmic contributions, we study in details how to exploit region observability in solving the transformed POMDP. To facilitate the study, we also propose a new exact algorithm for general POMDPs. The algorithm is conceptually simple and yet is significantly more efficient than all previous exact algorithms.

Connected through hyperedge HE-1 (induced by source paper "a method for speeding up value iteration in partially observable markov decision processes"):
  with members:
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "planning with partially observable markov decision processes advances in exact solution method"):
  with members:
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "restricted value iteration theory and algorithms"):
  with members:
    - "flexible decomposition algorithms for weakly coupled markov decision problems"
    - "structured reachability analysis for markov decision processes"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "policy iteration for factored mdps"
    - "speeding up the convergence of value iteration in partially observable markov decision processes"
    - "value function approximations for partially observable markov decision processes"
    - "the computational complexity of probabilistic planning"

Connected through hyperedge HE-4 (induced by source paper "speeding up the convergence of value iteration in partially observable markov decision processes"):
  with members:
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "a method for speeding up value iteration in partially observable markov decision processes"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-5 (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"
    - "planning with partially observable markov decision processes advances in exact solution method"

Connected through hyperedge HE-6 (induced by source paper "decision theoretic planning structural assumptions and computational leverage"):
  with members:
    - "on the complexity of solving markov decision problems"
    - "flexible decomposition algorithms for weakly coupled markov decision problems"
    - "exploiting the rule structure for decision making within the independent choice logic"
    - "integrating planning and execution in stochastic domains"
    - "abstracting probabilistic actions"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "structured reachability analysis for markov decision processes"

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)