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 predictive policy training using reinforcement learning"
  Abstract: Skilled robot task learning is best implemented by predictive action policies due to the inherent latency of sensorimotor processes. However, training such predictive policies is challenging as it involves finding a trajectory of motor activations for the full duration of the action. We propose a data-efficient deep predictive policy training (DPPT) framework with a deep neural network policy architecture which maps an image observation to a sequence of motor activations. The architecture consists of three sub-networks referred to as the perception, policy and behavior super-layers. The perception and behavior super-layers force an abstraction of visual and motor data trained with synthetic and simulated training samples, respectively. The policy super-layer is a small sub-network with fewer parameters that maps data in-between the abstracted manifolds. It is trained for each task using methods for policy search reinforcement learning. We demonstrate the suitability of the proposed architecture and learning framework by training predictive policies for skilled object grasping and ball throwing on a PR2 robot. The effectiveness of the method is illustrated by the fact that these tasks are trained using only about 180 real robot attempts with qualitative terminal rewards.

Connected through hyperedge HE-1 (induced by source paper "let s push things forward a survey on robot pushing"):
  with members:
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "feature based transfer learning for robotic push manipulation"
    - "a data efficient approach to precise and controlled pushing"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "a probabilistic data driven model for planar pushing"
    - "information theoretic model identification and policy search using physics engines with application to robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "transferable task execution from pixels through deep planning domain learning"):
  with members:
    - "representing robot task plans as robust logical dynamical systems"
    - "prospection interpretable plans from language by predicting the future"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "neural task graphs generalizing to unseen tasks from a single video demonstration"
    - "stripstream integrating symbolic planners and blackbox samplers"
    - "peorl integrating symbolic planning and hierarchical reinforcement learning for robust decision making"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "visual robot task planning"

Connected through hyperedge HE-3 (induced by source paper "robonet large scale multi robot learning"):
  with members:
    - "self supervised exploration via disagreement"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "manipulation by feel touch based control with deep predictive models"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "unsupervised visuomotor control through distributional planning networks"
    - "scaling autoregressive video models"
    - "exploration by random network distillation"

Connected through hyperedge HE-4 (induced by source paper "hierarchical foresight self supervised learning of long horizon tasks via visual subgoal generation"):
  with members:
    - "learning robotic manipulation through visual planning and acting"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "unsupervised visuomotor control through distributional planning networks"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "temporal difference models model free deep rl for model based control"
    - "learning actionable representations with goal conditioned policies"
    - "visual reinforcement learning with imagined goals"
    - "zero shot visual imitation"
    - "soft actor critic algorithms and applications"
    - "few shot goal inference for visuomotor learning and planning"
    - "unsupervised control through non parametric discriminative rewards"

Connected through hyperedge HE-6 (induced by source paper "self supervised correspondence in visuomotor policy learning"):
  with members:
    - "unsupervised learning of object keypoints for perception and control"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "graph structured visual imitation"
    - "a review of robot learning for manipulation challenges representations and algorithms"
    - "unsupervised visuomotor control through distributional planning networks"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"

Connected through hyperedge HE-7 (induced by source paper "generalization through simulation integrating simulated and real data into deep reinforcement learning for vision based autonomous flight"):
  with members:
    - "vr goggles for robots real to sim domain adaptation for visual control"
    - "deep drone racing learning agile flight in dynamic environments"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "gibson env real world perception for embodied agents"
    - "experiments in fast autonomous gps denied quadrotor flight"
    - "ultra low power deep learning powered autonomous nano drones"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"

Connected through hyperedge HE-8 (induced by source paper "adversarial feature training for generalizable robotic visuomotor control"):
  with members:
    - "contrastive adaptation network for unsupervised domain adaptation"
    - "affordance learning for end to end visuomotor robot control"
    - "beyond sharing weights for deep domain adaptation"
    - "grasp2vec learning object representations from self supervised grasping"
    - "deep reinforcement learning to acquire navigation skills for wheel legged robots in complex environments"
    - "deep object centric representations for generalizable robot learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"

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)