human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "first order decision diagrams for relational mdps"):
  Abstract: Markov decision processes capture sequential decision making under uncertainty, where an agent must choose actions so as to optimize long term reward. The paper studies efficient reasoning mechanisms for Relational Markov Decision Processes (RMDP) where world states have an internal relational structure that can be naturally described in terms of objects and relations among them. Two contributions are presented. First, the paper develops First Order Decision Diagrams (FODD), a new compact representation for functions over relational structures, together with a set of operators to combine FODDs, and novel reduction techniques to keep the representation small. Second, the paper shows how FODDs can be used to develop solutions for RMDPs, where reasoning is performed at the abstract level and the resulting optimal policy is independent of domain size (number of objects) or instantiation. In particular, a variant of the value iteration algorithm is developed by using special operations over FODDs, and the algorithm is shown to converge to the optimal policy.

Includes members:
  Member "spudd stochastic planning using decision diagrams":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maximum causal entropy specification inference from demonstrations")
  Member "exploiting first order regression in inductive policy selection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a heuristic search algorithm for solving first order mdps")
  Member "practical linear value approximation techniques for first order mdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a structured prediction approach for generalization in cooperative multi agent reinforcement learning")
  Member "policy iteration for relational mdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic planning and lifted inference")
  Member "approximate linear programming for first order mdps":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "practical linear value approximation techniques for first order mdps")
  Member "flucap a heuristic search planner for first order mdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the complexity of reasoning with fodd and gfodd")
  Member "approximate policy iteration with a policy language bias solving relational markov decision processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generalized neural policies for relational mdps")
  Member "relational dynamic bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic complex event recognition a survey")

Question: Please tell me which class the source paper 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)