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: "deep forward and inverse perceptual models for tracking and prediction"
  Abstract: We consider the problems of learning forward models that map state to high-dimensional images and inverse models that map high-dimensional images to state in robotics. Specifically, we present a perceptual model for generating video frames from state with deep networks, and provide a framework for its use in tracking and prediction tasks. We show that our proposed model greatly outperforms standard deconvolutional methods and GANs for image generation, producing clear, photo-realistic images. We also develop a convolutional neural network model for state estimation and compare the result to an Extended Kalman Filter to estimate robot trajectories. We validate all models on a real robotic system.

Connected through hyperedge HE-1 (induced by source paper "joint inference of kinematic and force trajectories with visuo tactile sensing"):
  with members:
    - "robust learning of tactile force estimation through robot interaction"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"
    - "realtime state estimation with tactile and visual sensing application to planar manipulation"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"

Connected through hyperedge HE-2 (induced by source paper "time reversal as self supervision"):
  with members:
    - "visual reinforcement learning with imagined goals"
    - "distributed distributional deterministic policy gradients"
    - "visual robot task planning"
    - "self supervised visual planning with temporal skip connections"
    - "hindsight experience replay"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "stochastic variational video prediction"
    - "reverse curriculum generation for reinforcement learning"

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)