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 "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  Abstract: Video prediction models combined with planning algorithms have shown promise in enabling robots to learn to perform many vision-based tasks through only self-supervision, reaching novel goals in cluttered scenes with unseen objects. However, due to the compounding uncertainty in long horizon video prediction and poor scalability of sampling-based planning optimizers, one significant limitation of these approaches is the ability to plan over long horizons to reach distant goals. To that end, we propose a framework for subgoal generation and planning, hierarchical visual foresight (HVF), which generates subgoal images conditioned on a goal image, and uses them for planning. The subgoal images are directly optimized to decompose the task into easy to plan segments, and as a result, we observe that the method naturally identifies semantically meaningful states as subgoals. Across three out of four simulated vision-based manipulation tasks, we find that our method achieves nearly a 200% performance improvement over planning without subgoals and model-free RL approaches. Further, our experiments illustrate that our approach extends to real, cluttered visual scenes. Project page: this https URL

Includes members:
  Member "learning robotic manipulation through visual planning and acting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions")
  Member "search on the replay buffer bridging planning and reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rewriting history with inverse rl hindsight inference for policy improvement")
  Member "learning latent plans from play":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "explore discover and learn unsupervised discovery of state covering skills")
  Member "divis domain invariant visual servoing for collision free goal reaching":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning view and target invariant visual servoing for navigation")
  Member "improvisation through physical understanding using novel objects as tools with visual foresight":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "goal conditioned end to end visuomotor control for versatile skill primitives")
  Member "robot motion planning in learned latent spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probably approximately correct vision based planning using motion primitives")
  Member "hierarchical reinforcement learning with hindsight":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "language as an abstraction for hierarchical deep reinforcement learning")
  Member "qt opt scalable deep reinforcement learning for vision based robotic manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)