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: "bayesian policy selection using active inference"
  Abstract: Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a well-known technique for learning such policies. However, current RL algorithms often have to deal with reward shaping, have difficulties generalizing to other environments and are most often sample inefficient. In this paper, we explore active inference and the free energy principle, a normative theory from neuroscience that explains how self-organizing biological systems operate by maintaining a model of the world and casting action selection as an inference problem. We apply this concept to a typical problem known to the RL community, the mountain car problem, and show how active inference encompasses both RL and learning from demonstrations.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning through active inference"):
  with members:
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "deep active inference as variational policy gradients"
    - "a unified bellman optimality principle combining reward maximization and empowerment"
    - "deep dynamics models for learning dexterous manipulation"
    - "demystifying active inference"
    - "model based reinforcement learning for atari"
    - "efficient exploration via state marginal matching"
    - "dream to control learning behaviors by latent imagination"

Connected through hyperedge HE-2 (induced by source paper "deep active inference for autonomous robot navigation"):
  with members:
    - "learning perception and planning with deep active inference"
    - "deep active inference as variational policy gradients"

Connected through hyperedge HE-3 (induced by source paper "learning perception and planning with deep active inference"):
  with members:
    - "deep active inference as variational policy gradients"

Connected through hyperedge HE-4 (induced by source paper "scaling active inference"):
  with members:
    - "deep active inference as variational policy gradients"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "model based reinforcement learning for atari"
    - "bootstrapping the expressivity with model based planning"
    - "uncertainty aware model based policy optimization"
    - "a survey on intrinsic motivation in reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "information maximizing exploration with a latent dynamics model"

Connected through hyperedge HE-5 (induced by source paper "deep active inference as variational policy gradients"):
  with members:
    - "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"
    - "boltzmann exploration done right"

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)