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 "planning with noisy probabilistic relational rules"):
  Abstract: Noisy probabilistic relational rules are a promising world model representation for several reasons. They are compact and generalize over world instantiations. They are usually interpretable and they can be learned effectively from the action experiences in complex worlds. We investigate reasoning with such rules in grounded relational domains. Our algorithms exploit the compactness of rules for efficient and fexible decision-theoretic planning. As a first approach, we combine these rules with the Upper Confidence Bounds applied to Trees (UCT) algorithm based on look-ahead trees. Our second approach converts these rules into a structured dynamic Bayesian network representation and predicts the effects of action sequences using approximate inference and beliefs over world states. We evaluate the effectiveness of our approaches for planning in a simulated complex 3D robot manipulation scenario with an articulated manipulator and realistic physics and in domains of the probabilistic planning competition. Empirical results show that our methods can solve problems where existing methods fail.

Includes members:
  Member "a heuristic search algorithm for solving first order mdps":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practical linear value approximation techniques for first order mdps")
  Member "the factored frontier algorithm for approximate inference in dbns":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reasoning about hypothetical agent behaviours and their parameters")
  Member "exploiting first order regression in inductive policy selection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a heuristic search algorithm for solving first order mdps")
  Member "first order decision diagrams for relational mdps":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "qualitative numeric planning reductions and complexity")
  Member "learning symbolic models of stochastic domains":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pddlgym gym environments from pddl problems")
  Member "probabilistic planning via heuristic forward search and weighted model counting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the approximability of weighted model integration on dnf structures")
  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 "the ff planning system fast plan generation through heuristic search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "what should i do now marrying reinforcement learning and symbolic planning")

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)