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: "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

Connected through hyperedge HE-1 (induced by source paper "goal conditioned end to end visuomotor control for versatile skill primitives"):
  with members:
    - "monte carlo tree search for efficient visually guided rearrangement planning"
    - "object centric forward modeling for model predictive control"
    - "good robot efficient reinforcement learning for multi step visual tasks via reward shaping"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "universal planning networks"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"

Connected through hyperedge HE-2 (induced by source paper "hallucinative topological memory for zero shot visual planning"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning robotic manipulation through visual planning and acting"
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "model based reinforcement learning for atari"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"

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)