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: "value directed belief state approximation for pomdps"
  Abstract: We consider the problem belief-state monitoring for the purposes of implementing a policy for a partially-observable Markov decision process (POMDP), specifically how one might approximate the belief state. Other schemes for belief-state approximation (e.g., based on minimixing a measures such as KL-diveregence between the true and estimated state) are not necessarily appropriate for POMDPs. Instead we propose a framework for analyzing value-directed approximation schemes, where approximation quality is determined by the expected error in utility rather than by the error in the belief state itself. We propose heuristic methods for finding good projection schemes for belief state estimation - exhibiting anytime characteristics - given a POMDP value fucntion. We also describe several algorithms for constructing bounds on the error in decision quality (expected utility) associated with acting in accordance with a given belief state approximation.

Connected through hyperedge HE-1 (induced by source paper "exploiting causality for selective belief filtering in dynamic bayesian networks"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "causal independence for knowledge acquisition and inference"
    - "tractable inference for complex stochastic processes"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "d separation from theorems to algorithms"
    - "the factored frontier algorithm for approximate inference in dbns"

Connected through hyperedge HE-2 (induced by source paper "on the linear belief compression of pomdps a re examination of current methods"):
  with members:
    - "point based pomdp algorithms improved analysis and implementation"
    - "heuristic search value iteration for pomdps"
    - "perseus randomized point based value iteration for pomdps"
    - "restricted value iteration theory and algorithms"
    - "value function approximations for partially observable markov decision processes"

Connected through hyperedge HE-3 (induced by source paper "vector space analysis of belief state approximation for pomdps"):
  with members:
    - "value directed sampling methods for pomdps"
    - "tractable inference for complex stochastic processes"
    - "incremental pruning a simple fast exact method for partially observable markov decision processes"
    - "value function approximations for partially observable markov decision processes"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-4 (induced by source paper "value directed sampling methods for pomdps"):
  with members:
    - "vector space analysis of belief state approximation for pomdps"
    - "stochastic simulation algorithms for dynamic probabilistic networks"
    - "structured arc reversal and simulation of dynamic probabilistic networks"
    - "rao blackwellised particle filtering for dynamic bayesian networks"

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)