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 "learning symbolic models of stochastic domains"):
  Abstract: In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics.

Includes members:
  Member "learning bayesian networks with local structure":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a meta transfer objective for learning to disentangle causal mechanisms")
  Member "tractable inference for complex stochastic processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "explaining deep neural networks with a polynomial time algorithm for shapley values approximation")
  Member "inductive policy selection for first order mdps":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural logic reinforcement learning")
  Member "logical hidden markov models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "state space abstractions for probabilistic inference a systematic review")
  Member "relational dynamic bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic complex event recognition a survey")
  Member "efficient solution algorithms for factored mdps":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "policy synthesis for factored mdps with graph temporal logic specifications")

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.LG(Machine Learning)