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 "regularizing trajectory optimization with denoising autoencoders"):
  Abstract: Trajectory optimization with learned dynamics models can often suffer from erroneous predictions of out-of-distribution trajectories. We propose to regularize trajectory optimization by means of a denoising autoencoder that is trained on the same trajectories as the dynamics model. We visually demonstrate the effectiveness of the regularization in gradient-based trajectory optimization for open-loop control of an industrial process. We compare with recent model-based reinforcement learning algorithms on a set of popular motor control tasks to demonstrate that the denoising regularization enables state-of-the-art sample-efficiency. We demonstrate the efficacy of the proposed method in regularizing both gradient-based and gradient-free trajectory optimization.

Includes members:
  Member "pipps flexible model based policy search robust to the curse of chaos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dream to control learning behaviors by latent imagination")
  Member "temporal difference models model free deep rl for model based control":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "model ensemble trust region policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ready policy one world building through active learning")
  Member "improving model based control and active exploration with reconstruction uncertainty optimization":
    also connected through other hyperedges:
      (none)
  Member "plan online learn offline efficient learning and exploration via model based control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural lyapunov model predictive control")
  Member "addressing function approximation error in actor critic methods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self supervised discovering of causal features towards interpretable reinforcement learning")
  Member "deep reinforcement learning in a handful of trials using probabilistic dynamics models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "model based reinforcement learning via meta policy optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ready policy one world building through active 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)