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: "approximate linear programming for first order mdps"
  Abstract: We introduce a new approximate solution technique for first-order Markov decision processes (FOMDPs). Representing the value function linearly w.r.t. a set of first-order basis functions, we compute suitable weights by casting the corresponding optimization as a first-order linear program and show how off-the-shelf theorem prover and LP software can be effectively used. This technique allows one to solve FOMDPs independent of a specific domain instantiation; furthermore, it allows one to determine bounds on approximation error that apply equally to all domain instantiations. We apply this solution technique to the task of elevator scheduling with a rich feature space and multi-criteria additive reward, and demonstrate that it outperforms a number of intuitive, heuristicallyguided policies.

Connected through hyperedge HE-1 (induced by source paper "practical linear value approximation techniques for first order mdps"):
  with members:
    - "a heuristic search algorithm for solving first order mdps"
    - "exploiting first order regression in inductive policy selection"
    - "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-2 (induced by source paper "policy iteration for relational mdps"):
  with members:
    - "first order decision diagrams for relational mdps"

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"
    - "policy iteration 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"
    - "relational dynamic bayesian networks"
    - "efficient solution algorithms for factored mdps"

Connected through hyperedge HE-4 (induced by source paper "solving factored mdps with hybrid state and action variables"):
  with members:
    - "from influence diagrams to junction trees"
    - "approximating map using local search"
    - "metrics for finite markov decision processes"
    - "dynamic programming for structured continuous markov decision problems"
    - "planning under continuous time and resource uncertainty a challenge for ai"
    - "solving map exactly using systematic search"
    - "metrics for markov decision processes with infinite state spaces"
    - "solving factored mdps with continuous and discrete variables"

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)