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 dexterous manipulation for a soft robotic hand from human demonstration"
  Abstract: Dexterous multi-fingered hands can accomplish fine manipulation behaviors that are infeasible with simple robotic grippers. However, sophisticated multi-fingered hands are often expensive and fragile. Low-cost soft hands offer an appealing alternative to more conventional devices, but present considerable challenges in sensing and actuation, making them difficult to apply to more complex manipulation tasks. In this paper, we describe an approach to learning from demonstration that can be used to train soft robotic hands to perform dexterous manipulation tasks. Our method uses object-centric demonstrations, where a human demonstrates the desired motion of manipulated objects with their own hands, and the robot autonomously learns to imitate these demonstrations using reinforcement learning. We propose a novel algorithm that allows us to blend and select a subset of the most feasible demonstrations to learn to imitate on the hardware, which we use with an extension of the guided policy search framework to use multiple demonstrations to learn generalizable neural network policies. We demonstrate our approach on the RBO Hand 2, with learned motor skills for turning a valve, manipulating an abacus, and grasping.

Connected through hyperedge HE-1 (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"
    - "learning robotic manipulation through visual planning and acting"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "contact less manipulation of millimeter scale objects via ultrasonic levitation"):
  with members:
    - "improved proximity contact and force sensing via optimization of elastomer air interface geometry"
    - "a framework for robot manipulation skill formalism meta learning and adaptive control"
    - "a compliant underactuated hand for robust manipulation"

Connected through hyperedge HE-3 (induced by source paper "reinforcement and imitation learning for diverse visuomotor skills"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "one shot visual imitation learning via meta learning"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"
    - "progressive neural networks"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

Connected through hyperedge HE-4 (induced by source paper "one shot observation learning using visual activity features"):
  with members:
    - "playing hard exploration games by watching youtube"
    - "zero shot visual imitation"
    - "defining the problem of observation learning"
    - "pushing fast and slow task adaptive mpc for pushing manipulation under uncertainty"
    - "one shot hierarchical imitation learning of compound visuomotor tasks"
    - "one shot imitation from observing humans via domain adaptive meta learning"
    - "deep reinforcement learning that matters"
    - "imitation from observation learning to imitate behaviors from raw video via context translation"

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 "dexterous manipulation with deep reinforcement learning efficient general and low cost"):
  with members:
    - "learning dexterous in hand manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "transferring end to end visuomotor control from simulation to real world for a multi stage task"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "towards generalization and simplicity in continuous control"
    - "overcoming exploration in reinforcement learning with demonstrations"

Connected through hyperedge HE-7 (induced by source paper "data efficient deep reinforcement learning for dexterous manipulation"):
  with members:
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "asynchronous methods for deep reinforcement learning"
    - "guided cost learning deep inverse optimal control via policy optimization"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-8 (induced by source paper "cloth manipulation using random forest based imitation learning"):
  with members:
    - "cloth manipulation using random forest based controller parametrization"
    - "learning visual servoing with deep features and fitted q iteration"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "multi sensor surface analysis for robotic ironing"
    - "folding deformable objects using predictive simulation and trajectory optimization"
    - "end to end training of deep visuomotor policies"
    - "a reduction of imitation learning and structured prediction to no regret online 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.LG(Machine Learning)