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: "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
  Abstract: Motivated by recent advances in Deep Learning for robot control, this paper considers two learning algorithms in terms of how they acquire demonstrations. "Human-Centric" (HC) sampling is the standard supervised learning algorithm, where a human supervisor demonstrates the task by teleoperating the robot to provide trajectories consisting of state-control pairs. "Robot-Centric" (RC) sampling is an increasingly popular alternative used in algorithms such as DAgger, where a human supervisor observes the robot executing a learned policy and provides corrective control labels for each state visited. RC sampling can be challenging for human supervisors and prone to mislabeling. RC sampling can also induce error in policy performance because it repeatedly visits areas of the state space that are harder to learn. Although policies learned with RC sampling can be superior to HC sampling for standard learning models such as linear SVMs, policies learned with HC sampling may be comparable with highly-expressive learning models such as deep learning and hyper-parametric decision trees, which have little model error. We compare HC and RC using a grid world and a physical robot singulation task, where in the latter the input is a binary image of a connected set of objects on a planar worksurface and the policy generates a motion of the gripper to separate one object from the rest. We observe in simulation that for linear SVMs, policies learned with RC outperformed those learned with HC but that with deep models this advantage disappears. We also find that with RC, the corrective control labels provided by humans can be highly inconsistent. We prove there exists a class of examples where in the limit, HC is guaranteed to converge to an optimal policy while RC may fail to converge.

Connected through hyperedge HE-1 (induced by source paper "learning to singulate objects using a push proposal network"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "metric learning for generalizing spatial relations to new objects"
    - "interactive perception leveraging action in perception and perception in action"
    - "fast model identification via physics engines for data efficient policy search"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "deep visual foresight for planning robot motion"
    - "high precision grasp pose detection in dense clutter"
    - "se3 nets learning rigid body motion using deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "imitation learning as f divergence minimization"):
  with members:
    - "an algorithmic perspective on imitation learning"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "adversarial imitation via variational inverse reinforcement learning"
    - "sfv reinforcement learning of physical skills from videos"
    - "generative adversarial imitation from observation"
    - "sample efficient imitation learning via generative adversarial nets"
    - "variational discriminator bottleneck improving imitation learning inverse rl and gans by constraining information flow"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-3 (induced by source paper "on policy robot imitation learning from a converging supervisor"):
  with members:
    - "extending deep model predictive control with safety augmented value estimation from demonstrations"
    - "extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations"
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "dual policy iteration"
    - "model ensemble trust region policy optimization"
    - "addressing function approximation error in actor critic methods"
    - "convergence of value aggregation for imitation learning"

Connected through hyperedge HE-4 (induced by source paper "dynamic regret convergence analysis and an adaptive regularization algorithm for on policy robot imitation learning"):
  with members:
    - "introduction to online convex optimization"
    - "online learning with continuous variations dynamic regret and reductions"
    - "imitation learning as f divergence minimization"
    - "an algorithmic perspective on imitation learning"
    - "lipschitz continuity in model based reinforcement learning"
    - "convergence of value aggregation for imitation learning"
    - "a dynamic regret analysis and adaptive regularization algorithm for on policy robot imitation learning"
    - "accelerating imitation learning with predictive models"

Connected through hyperedge HE-5 (induced by source paper "roboturk a crowdsourcing platform for robotic skill learning through imitation"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "backplay man muss immer umkehren"
    - "learning task oriented grasping for tool manipulation from simulated self supervision"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "know what you don t know unanswerable questions for squad"
    - "baxter s homunculus virtual reality spaces for teleoperation in manufacturing"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"

Connected through hyperedge HE-6 (induced by source paper "learning task oriented grasping for tool manipulation from simulated self supervision"):
  with members:
    - "learning to singulate objects using a push proposal network"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "grasp pose detection in point clouds"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "affordancenet an end to end deep learning approach for object affordance detection"
    - "learning a visuomotor controller for real world robotic grasping using easily simulated depth images"
    - "end to end learning of semantic grasping"

Connected through hyperedge HE-7 (induced by source paper "chauffeurnet learning to drive by imitating the best and synthesizing the worst"):
  with members:
    - "conditional affordance learning for driving in urban environments"
    - "driving policy transfer via modularity and abstraction"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "end to end learning of driving models with surround view cameras and route planners"
    - "virtual to real reinforcement learning for autonomous driving"
    - "carla an open urban driving simulator"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "imitating driver behavior with generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "shared autonomy via deep reinforcement learning"):
  with members:
    - "learning models for shared control of human machine systems with unknown dynamics"
    - "rainbow combining improvements in deep reinforcement learning"
    - "explore exploit or listen combining human feedback and policy model to speed up deep reinforcement learning in 3d worlds"
    - "deep tamer interactive agent shaping in high dimensional state spaces"
    - "human robot mutual adaptation in shared autonomy"
    - "learning language games through interaction"
    - "deep recurrent q learning for partially observable mdps"
    - "deep reinforcement learning with double q 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)