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 inference and stochastic optimal control"
  Abstract: We propose a novel reformulation of the stochastic optimal control problem as an approximate inference problem, demonstrating, that such a interpretation leads to new practical methods for the original problem. In particular we characterise a novel class of iterative solutions to the stochastic optimal control problem based on a natural relaxation of the exact dual formulation. These theoretical insights are applied to the Reinforcement Learning problem where they lead to new model free, o policy methods for discrete and continuous problems.

Connected through hyperedge HE-1 (induced by source paper "deep active inference as variational policy gradients"):
  with members:
    - "bayesian policy selection using active inference"
    - "virel a variational inference framework for reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "expanding the active inference landscape more intrinsic motivations in the perception action loop"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "unsupervised predictive memory in a goal directed agent"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-2 (induced by source paper "virel a variational inference framework for reinforcement learning"):
  with members:
    - "a theoretical analysis of deep q learning"
    - "a theory of regularized markov decision processes"
    - "maven multi agent variational exploration"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "fourier policy gradients"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "dice the infinitely differentiable monte carlo estimator"

Connected through hyperedge HE-3 (induced by source paper "continuous deep q learning with model based acceleration"):
  with members:
    - "prioritized experience replay"
    - "high dimensional continuous control using generalized advantage estimation"
    - "one shot learning of manipulation skills with online dynamics adaptation and neural network priors"
    - "continuous control with deep reinforcement learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "trust region policy optimization"
    - "dueling network architectures for deep reinforcement learning"
    - "deep reinforcement learning in parameterized action space"

Connected through hyperedge HE-4 (induced by source paper "taming the noise in reinforcement learning via soft updates"):
  with members:
    - "increasing the action gap new operators for reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "dynamic policy programming"

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.LG(Machine Learning)