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: "correlated action effects in decision theoretic regression"
  Abstract: Much recent research in decision theoretic planning has adopted Markov decision processes (MDPs) as the model of choice, and has attempted to make their solution more tractable by exploiting problem structure. One particular algorithm, structured policy construction achieves this by means of a decision theoretic analog of goal regression using action descriptions based on Bayesian networks with tree-structured conditional probability tables. The algorithm as presented is not able to deal with actions with correlated effects. We describe a new decision theoretic regression operator that corrects this weakness. While conceptually straightforward, this extension requires a somewhat more complicated technical approach.

Connected through hyperedge HE-1 (induced by source paper "spudd stochastic planning using decision diagrams"):
  with members:
    - "exploiting the rule structure for decision making within the independent choice logic"
    - "topological parameters for time space tradeoff"
    - "context specific independence in bayesian networks"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-2 (induced by source paper "proximity based non uniform abstractions for approximate planning"):
  with members:
    - "continuous value function approximation for sequential bidding policies"
    - "hierarchical solution of markov decision processes using macro actions"
    - "spudd stochastic planning using decision diagrams"
    - "macro ff improving ai planning with automatically learned macro operators"
    - "efficient solution algorithms for factored mdps"
    - "hierarchical reinforcement learning with the maxq value function decomposition"

Connected through hyperedge HE-3 (induced by source paper "exploiting contextual independence in probabilistic inference"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "exploiting the rule structure for decision making within the independent choice logic"
    - "a bayesian approach to learning bayesian networks with local structure"
    - "learning bayesian networks with local structure"
    - "a new look at causal independence"
    - "context specific independence in bayesian networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "mean field theory for sigmoid belief 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)