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: "learning robotic manipulation through visual planning and acting"
  Abstract: Planning for robotic manipulation requires reasoning about the changes a robot can affect on objects. When such interactions can be modelled analytically, as in domains with rigid objects, efficient planning algorithms exist. However, in both domestic and industrial domains, the objects of interest can be soft, or deformable, and hard to model analytically. For such cases, we posit that a data-driven modelling approach is more suitable. In recent years, progress in deep generative models has produced methods that learn to `imagine' plausible images from data. Building on the recent Causal InfoGAN generative model, in this work we learn to imagine goal-directed object manipulation directly from raw image data of self-supervised interaction of the robot with the object. After learning, given a goal observation of the system, our model can generate an imagined plan -- a sequence of images that transition the object into the desired goal. To execute the plan, we use it as a reference trajectory to track with a visual servoing controller, which we also learn from the data as an inverse dynamics model. In a simulated manipulation task, we show that separating the problem into visual planning and visual tracking control is more sample efficient and more interpretable than alternative data-driven approaches. We further demonstrate our approach on learning to imagine and execute in 3 environments, the final of which is deformable rope manipulation on a PR2 robot.

Connected through hyperedge HE-1 (induced by source paper "plannable approximations to mdp homomorphisms equivariance under actions"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "mastering atari go chess and shogi by planning with a learned model"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "unsupervised state representation learning in atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "efficient model based deep reinforcement learning with variational state tabulation"
    - "composable planning with attributes"
    - "combined reinforcement learning via abstract representations"

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 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"
    - "learning plannable representations with causal infogan"

Connected through hyperedge HE-3 (induced by source paper "domain adversarial and conditional state space model for imitation learning"):
  with members:
    - "to follow or not to follow selective imitation learning from observations"
    - "variational inference mpc for bayesian model based reinforcement learning"
    - "integration of imitation learning using gail and reinforcement learning using task achievement rewards via probabilistic generative model"
    - "learning belief representations for imitation learning in pomdps"
    - "stochastic latent actor critic deep reinforcement learning with a latent variable model"
    - "recent advances in imitation learning from observation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-4 (induced by source paper "learning when to trust a dynamics model for planning in reduced state spaces"):
  with members:
    - "manipulating deformable objects by interleaving prediction planning and control"
    - "robot motion planning in learned latent spaces"
    - "learning latent space dynamics for tactile servoing"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "robust locally linear controllable embedding"
    - "deep visual foresight for planning robot motion"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "learning rope manipulation policies using dense object descriptors trained on synthetic depth data"):
  with members:
    - "safety augmented value estimation from demonstrations saved safe deep model based rl for sparse cost robotic tasks"
    - "grasp2vec learning object representations from self supervised grasping"
    - "dense object nets learning dense visual object descriptors by and for robotic manipulation"
    - "combining self supervised learning and imitation for vision based rope manipulation"
    - "learning compact geometric features"
    - "mask r cnn"

Connected through hyperedge HE-6 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "when to trust your model model based policy optimization"

Connected through hyperedge HE-7 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  with members:
    - "search on the replay buffer bridging planning and reinforcement learning"
    - "learning latent plans from play"
    - "divis domain invariant visual servoing for collision free goal reaching"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "robot motion planning in learned latent spaces"
    - "hierarchical reinforcement learning with hindsight"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "data efficient hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "learning to manipulate deformable objects without demonstrations"):
  with members:
    - "deep imitation learning of sequential fabric smoothing policies"
    - "deep transfer learning of pick points on fabric for robot bed making"
    - "rlpyt a research code base for deep reinforcement learning in pytorch"
    - "robot bed making deep transfer learning using depth sensing of deformable fabric"
    - "deepmind control suite"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "learning based feedback controller for deformable object manipulation"

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)