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: "implementation and comparison of solution methods for decision processes with non markovian rewards"
  Abstract: This paper examines a number of solution methods for decision processes with non-Markovian rewards (NMRDPs). They all exploit a temporal logic specification of the reward function to automatically translate the NMRDP into an equivalent Markov decision process (MDP) amenable to well-known MDP solution methods. They differ however in the representation of the target MDP and the class of MDP solution methods to which they are suited. As a result, they adopt different temporal logics and different translations. Unfortunately, no implementation of these methods nor experimental let alone comparative results have ever been reported. This paper is the first step towards filling this gap. We describe an integrated system for solving NMRDPs which implements these methods and several variants under a common interface; we use it to compare the various approaches and identify the problem features favoring one over the other.

Connected through hyperedge HE-1 (induced by source paper "automated experiment design for data efficient verification of parametric markov decision processes"):
  with members:
    - "parameter synthesis for markov models faster than ever"
    - "smart sampling for lightweight verification of markov decision processes"

Connected through hyperedge HE-2 (induced by source paper "from preference based to multiobjective sequential decision making"):
  with members:
    - "contextual dueling bandits"
    - "a survey of multi objective sequential decision making"
    - "approximation of lorenz optimal solutions in multiobjective markov decision processes"
    - "april active preference learning based reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "exploiting first order regression in inductive policy selection"):
  with members:
    - "spudd stochastic planning using decision diagrams"
    - "inductive policy selection for first order mdps"
    - "otter 3 3 reference manual"

Connected through hyperedge HE-4 (induced by source paper "decision theoretic planning with non markovian rewards"):
  with members:
    - "a heuristic search algorithm for solving first order mdps"
    - "spudd stochastic planning using decision diagrams"
    - "symbolic generalization for on line planning"
    - "engineering a conformant probabilistic planner"
    - "mgpt a probabilistic planner based on heuristic search"
    - "the ff planning system fast plan generation through heuristic search"
    - "decision theoretic planning structural assumptions and computational leverage"

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)