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 a structured neural network policy for a hopping task"
  Abstract: In this letter, we present a method for learning a reactive policy for a simple dynamic locomotion task involving hard impact and switching contacts where we assume the contact location and contact timing to be unknown. To learn such a policy, we use optimal control to optimize a local controller for a fixed environment and contacts. We learn the contact-rich dynamics for our underactuated systems along these trajectories in a sample efficient manner. We use the optimized policies to learn the reactive policy in form of a neural network. Using a new neural network architecture, we are able to preserve more information from the local policy and make its output interpretable in the sense that its output in terms of desired trajectories, feedforward commands and gains can be interpreted. Extensive simulations demonstrate the robustness of the approach to changing environments, outperforming a model-free gradient policy based methods on the same tasks in simulation. Finally, we show that the learned policy can be robustly transferred on a real robot.

Connected through hyperedge HE-1 (induced by source paper "variable impedance control in end effector space an action space for reinforcement learning in contact rich tasks"):
  with members:
    - "soft actor critic algorithms and applications"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "proximal policy optimization algorithms"
    - "deep reinforcement learning for robotic manipulation with asynchronous off policy updates"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "an open torque controlled modular robot architecture for legged locomotion research"):
  with members:
    - "stanford doggo an open source quasi direct drive quadruped"
    - "quasi direct drive for low cost compliant robotic manipulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "oncilla robot a versatile open source quadruped research robot with compliant pantograph legs"
    - "cable driven actuation for highly dynamic robotic systems"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "structured contact force optimization for kino dynamic motion generation"

Connected through hyperedge HE-3 (induced by source paper "learning variable impedance control for contact sensitive tasks"):
  with members:
    - "variable impedance control in end effector space an action space for reinforcement learning in contact rich tasks"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning locomotion skills using deeprl does the choice of action space matter"
    - "momentum control with hierarchical inverse dynamics on a torque controlled humanoid"
    - "continuous control with deep reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "locomotion planning through a hybrid bayesian trajectory optimization"):
  with members:
    - "sequential linear quadratic optimal control for nonlinear switched systems"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "reactive planar manipulation with convex hybrid mpc"
    - "an efficient optimal planning and control framework for quadrupedal locomotion"

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)