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 "scaling active inference"):
  Abstract: In reinforcement learning (RL), agents often operate in partially observed and uncertain environments. Model-based RL suggests that this is best achieved by learning and exploiting a probabilistic model of the world. 'Active inference' is an emerging normative framework in cognitive and computational neuroscience that offers a unifying account of how biological agents achieve this. On this framework, inference, learning and action emerge from a single imperative to maximize the Bayesian evidence for a niched model of the world. However, implementations of this process have thus far been restricted to low-dimensional and idealized situations. Here, we present a working implementation of active inference that applies to high-dimensional tasks, with proof-of-principle results demonstrating efficient exploration and an order of magnitude increase in sample efficiency over strong model-free baselines. Our results demonstrate the feasibility of applying active inference at scale and highlight the operational homologies between active inference and current model-based approaches to RL.

Includes members:
  Member "deep active inference as variational policy gradients":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reinforcement learning through active inference")
  Member "mastering atari go chess and shogi by planning with a learned model":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neuroevolution of self interpretable agents")
  Member "model based reinforcement learning for atari":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "bootstrapping the expressivity with model based planning":
    also connected through other hyperedges:
      (none)
  Member "bayesian policy selection using active inference":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reinforcement learning through active inference")
  Member "uncertainty aware model based policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "safety guarantees for planning based on iterative gaussian processes")
  Member "a survey on intrinsic motivation in reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "social navigation with human empowerment driven reinforcement learning")
  Member "learning latent dynamics for planning from pixels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")

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)