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 policies from experience and imitation"
  Abstract: We explore learning-based approaches for feedback control of a dexterous five-finger hand performing non-prehensile manipulation. First, we learn local controllers that are able to perform the task starting at a predefined initial state. These controllers are constructed using trajectory optimization with respect to locally-linear time-varying models learned directly from sensor data. In some cases, we initialize the optimizer with human demonstrations collected via teleoperation in a virtual environment. We demonstrate that such controllers can perform the task robustly, both in simulation and on the physical platform, for a limited range of initial conditions around the trained starting state. We then consider two interpolation methods for generalizing to a wider range of initial conditions: deep learning, and nearest neighbors. We find that nearest neighbors achieve higher performance. Nevertheless, the neural network has its advantages: it uses only tactile and proprioceptive feedback but no visual feedback about the object (i.e. it performs the task blind) and learns a time-invariant policy. In contrast, the nearest neighbors method switches between time-varying local controllers based on the proximity of initial object states sensed via motion capture. While both generalization methods leave room for improvement, our work shows that (i) local trajectory-based controllers for complex non-prehensile manipulation tasks can be constructed from surprisingly small amounts of training data, and (ii) collections of such controllers can be interpolated to form more global controllers. Results are summarized in the supplementary video: this https URL

Connected through hyperedge HE-1 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"

Connected through hyperedge HE-2 (induced by source paper "learning to combine primitive skills a step towards versatile robotic manipulation"):
  with members:
    - "learning to augment synthetic images for sim2real policy transfer"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "latent space policies for hierarchical reinforcement learning"
    - "hierarchical imitation and reinforcement learning"
    - "neural modular control for embodied question answering"

Connected through hyperedge HE-3 (induced by source paper "end to end robotic reinforcement learning without reward engineering"):
  with members:
    - "risk aware active inverse reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "variational inverse control with events a general framework for data driven reward definition"
    - "learning dexterous in hand manipulation"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-4 (induced by source paper "reusable neural skill embeddings for vision guided whole body movement and object manipulation"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "iterative reinforcement learning based design of dynamic locomotion skills for cassie"
    - "robust recovery controller for a quadrupedal robot using deep reinforcement learning"
    - "learning latent plans from play"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "learning agile and dynamic motor skills for legged robots"
    - "hierarchical visuomotor control of humanoids"

Connected through hyperedge HE-5 (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-6 (induced by source paper "learning dexterous in hand manipulation"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "on policy learning robust to irreversible events an application to robotic in hand manipulation"
    - "distributed distributional deterministic policy gradients"

Connected through hyperedge HE-7 (induced by source paper "task oriented hand motion retargeting for dexterous manipulation imitation"):
  with members:
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "neural kinematic networks for unsupervised motion retargetting"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "occlusion aware hand pose estimation using hierarchical mixture density network"
    - "one shot visual imitation learning via meta learning"
    - "bighand2 2m benchmark hand pose dataset and state of the art analysis"
    - "overcoming exploration in reinforcement learning with demonstrations"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"

Connected through hyperedge HE-8 (induced by source paper "leveraging contact forces for learning to grasp"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "grasp pose detection in point clouds"
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "reinforcement learning for pivoting task"
    - "the feeling of success does touch sensing help predict grasp outcomes"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "real time perception meets reactive motion generation"

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)