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: "policy iteration for relational mdps"
  Abstract: Relational Markov Decision Processes are a useful abstraction for complex reinforcement learning problems and stochastic planning problems. Recent work developed representation schemes and algorithms for planning in such problems using the value iteration algorithm. However, exact versions of more complex algorithms, including policy iteration, have not been developed or analyzed. The paper investigates this potential and makes several contributions. First we observe two anomalies for relational representations showing that the value of some policies is not well defined or cannot be calculated for restricted representation schemes used in the literature. On the other hand, we develop a variant of policy iteration that can get around these anomalies. The algorithm includes an aspect of policy improvement in the process of policy evaluation and thus differs from the original algorithm. We show that despite this difference the algorithm converges to the optimal policy.

Connected through hyperedge HE-1 (induced by source paper "stochastic planning and lifted inference"):
  with members:
    - "the complexity of reasoning with fodd and gfodd"
    - "solving relational mdps with exogenous events and additive rewards"
    - "spudd stochastic planning using decision diagrams"
    - "belief propagation for structured decision making"
    - "first order decision diagrams for relational mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-2 (induced by source paper "probabilistic relational planning with first order decision diagrams"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "exploiting first order regression in inductive policy selection"
    - "first order decision diagrams for relational mdps"
    - "flucap a heuristic search planner for first order mdps"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-3 (induced by source paper "first order decision diagrams for relational mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "exploiting first order regression in inductive policy selection"
    - "practical linear value approximation techniques for first order mdps"
    - "approximate linear programming for first order mdps"
    - "flucap a heuristic search planner for first order mdps"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "relational dynamic bayesian networks"
    - "efficient solution algorithms for factored mdps"

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)