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: "exploiting first order regression in inductive policy selection"
  Abstract: We consider the problem of computing optimal generalised policies for relational Markov decision processes. We describe an approach combining some of the benefits of purely inductive techniques with those of symbolic dynamic programming methods. The latter reason about the optimal value function using first-order decision theoretic regression and formula rewriting, while the former, when provided with a suitable hypotheses language, are capable of generalising value functions or policies for small instances. Our idea is to use reasoning and in particular classical first-order regression to automatically generate a hypotheses language dedicated to the domain at hand, which is then used as input by an inductive solver. This approach avoids the more complex reasoning of symbolic dynamic programming while focusing the inductive solver's attention on concepts that are specifically relevant to the optimal value function for the domain considered.

Connected through hyperedge HE-1 (induced by source paper "a heuristic search algorithm for solving first order mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-2 (induced by source paper "practical linear value approximation techniques for first order mdps"):
  with members:
    - "a heuristic search algorithm for solving first order mdps"
    - "approximate linear programming for first order mdps"
    - "mgpt a probabilistic planner based on heuristic search"
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-3 (induced by source paper "approximate linear programming for first order mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "efficient solution algorithms for factored mdps"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-4 (induced by source paper "probabilistic relational planning with first order decision diagrams"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "policy iteration for relational mdps"
    - "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-5 (induced by source paper "planning with noisy probabilistic relational rules"):
  with members:
    - "a heuristic search algorithm for solving first order mdps"
    - "the factored frontier algorithm for approximate inference in dbns"
    - "first order decision diagrams for relational mdps"
    - "learning symbolic models of stochastic domains"
    - "probabilistic planning via heuristic forward search and weighted model counting"
    - "approximate policy iteration with a policy language bias solving relational markov decision processes"
    - "the ff planning system fast plan generation through heuristic search"
    - "decision theoretic planning structural assumptions and computational leverage"

Connected through hyperedge HE-6 (induced by source paper "automatic induction of bellman error features for probabilistic planning"):
  with members:
    - "inductive policy selection for first order mdps"
    - "practical linear value approximation techniques 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"
    - "the automatic inference of state invariants in tim"
    - "learning first order definitions of functions"

Connected through hyperedge HE-7 (induced by source paper "first order decision diagrams for relational mdps"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "practical linear value approximation techniques for first order mdps"
    - "policy iteration for relational 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"

Connected through hyperedge HE-8 (induced by source paper "approximate policy iteration with a policy language bias solving relational markov decision processes"):
  with members:
    - "model reduction techniques for computing approximately optimal solutions for markov decision processes"
    - "inductive policy selection for first order mdps"
    - "ordered landmarks in planning"
    - "efficient solution algorithms for factored mdps"
    - "the ff planning system fast plan generation through heuristic search"

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)