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: "visual foresight model based deep reinforcement learning for vision based robotic control"
  Abstract: Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains. We present a deep RL method that is practical for real-world robotics tasks, such as robotic manipulation, and generalizes effectively to never-before-seen tasks and objects. In these settings, ground truth reward signals are typically unavailable, and we therefore propose a self-supervised model-based approach, where a predictive model learns to directly predict the future from raw sensory readings, such as camera images. At test time, we explore three distinct goal specification methods: designated pixels, where a user specifies desired object manipulation tasks by selecting particular pixels in an image and corresponding goal positions, goal images, where the desired goal state is specified with an image, and image classifiers, which define spaces of goal states. Our deep predictive models are trained using data collected autonomously and continuously by a robot interacting with hundreds of objects, without human supervision. We demonstrate that visual MPC can generalize to never-before-seen objects---both rigid and deformable---and solve a range of user-defined object manipulation tasks using the same model.

Connected through hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding"):
  with members:
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "model based reinforcement learning for atari"
    - "adversarial video generation on complex datasets"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "learning latent dynamics for planning from pixels"
    - "learning plannable representations with causal infogan"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "world models"

Connected through hyperedge HE-2 (induced by source paper "learning predictive representations for deformable objects using contrastive estimation"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "deep imitation learning of sequential fabric smoothing policies"
    - "contrastive multiview coding"
    - "learning robotic manipulation through visual planning and acting"
    - "learning to manipulate deformable objects without demonstrations"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "learning latent dynamics for planning from pixels"
    - "learning dexterous in hand manipulation"

Connected through hyperedge HE-3 (induced by source paper "deep variational luenberger type observer for stochastic video prediction"):
  with members:
    - "video prediction for precipitation nowcasting"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "unsupervised discovery of parts structure and dynamics"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-4 (induced by source paper "active perception and representation for robotic manipulation"):
  with members:
    - "saccader improving accuracy of hard attention models for vision"
    - "model based reinforcement learning for atari"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning actionable representations from visual observations"
    - "visual reinforcement learning with imagined goals"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"

Connected through hyperedge HE-5 (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"
    - "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "disentangling controllable object through video prediction improves visual reinforcement learning"):
  with members:
    - "model based reinforcement learning for atari"
    - "learning latent dynamics for planning from pixels"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "contingency aware exploration in reinforcement learning"
    - "independently controllable features"
    - "curiosity driven exploration by self supervised prediction"
    - "unsupervised learning for physical interaction through video prediction"
    - "deep visual foresight for planning robot motion"

Connected through hyperedge HE-7 (induced by source paper "probably approximately correct vision based planning using motion primitives"):
  with members:
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "pytorch an imperative style high performance deep learning library"
    - "pac bayes with backprop"
    - "robot motion planning in learned latent spaces"
    - "universal planning networks"
    - "pac bayes control learning policies that provably generalize to novel environments"
    - "motion planning networks"
    - "pac bayes control synthesizing controllers that provably generalize to novel environments"

Connected through hyperedge HE-8 (induced by source paper "deep imitation learning of sequential fabric smoothing from an algorithmic supervisor"):
  with members:
    - "a grasping centered analysis for cloth manipulation"
    - "model based behavioral cloning with future image similarity learning"
    - "learning to manipulate deformable objects without demonstrations"
    - "dynamic cloth manipulation with deep reinforcement learning"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "deep haptic model predictive control for robot assisted dressing"
    - "fast and reliable autonomous surgical debridement with cable driven robots using a two phase calibration procedure"

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.RO(Robotics)