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 "reinforcement learning through active inference"):
  Abstract: The central tenet of reinforcement learning (RL) is that agents seek to maximize the sum of cumulative rewards. In contrast, active inference, an emerging framework within cognitive and computational neuroscience, proposes that agents act to maximize the evidence for a biased generative model. Here, we illustrate how ideas from active inference can augment traditional RL approaches by (i) furnishing an inherent balance of exploration and exploitation, and (ii) providing a more flexible conceptualization of reward. Inspired by active inference, we develop and implement a novel objective for decision making, which we term the free energy of the expected future. We demonstrate that the resulting algorithm successfully balances exploration and exploitation, simultaneously achieving robust performance on several challenging RL benchmarks with sparse, well-shaped, and no rewards.

Includes members:
  Member "variational inference mpc for bayesian model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain adversarial and conditional state space model for imitation learning")
  Member "deep active inference as variational policy gradients":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "end to end pixel based deep active inference for body perception and action")
  Member "a unified bellman optimality principle combining reward maximization and empowerment":
    also connected through other hyperedges:
      (none)
  Member "deep dynamics models for learning dexterous manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ready policy one world building through active learning")
  Member "demystifying active inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ready policy one world building through active learning")
  Member "model based reinforcement learning for atari":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "efficient exploration via state marginal matching":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "dream to control learning behaviors by latent imagination":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to fly via deep model based reinforcement learning")

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)