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 to navigate cloth using haptics"
  Abstract: We present a controller that allows an arm-like manipulator to navigate deformable cloth garments in simulation through the use of haptic information. The main challenge of such a controller is to avoid getting tangled in, tearing or punching through the deforming cloth. Our controller aggregates force information from a number of haptic-sensing spheres all along the manipulator for guidance. Based on haptic forces, each individual sphere updates its target location, and the conflicts that arise between this set of desired positions is resolved by solving an inverse kinematic problem with constraints. Reinforcement learning is used to train the controller for a single haptic-sensing sphere, where a training run is terminated (and thus penalized) when large forces are detected due to contact between the sphere and a simplified model of the cloth. In simulation, we demonstrate successful navigation of a robotic arm through a variety of garments, including an isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two baseline controllers: one without haptics and another that was trained based on large forces between the sphere and cloth, but without early termination.

Connected through hyperedge HE-1 (induced by source paper "curriculum learning for reinforcement learning domains a framework and survey"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "an optimization framework for task sequencing in curriculum learning"
    - "curriculum learning for cumulative return maximization"
    - "evolutionarily curated curriculum learning for deep reinforcement learning agents"
    - "starcraft micromanagement with reinforcement learning and curriculum transfer learning"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "learning curriculum policies for reinforcement learning"
    - "screenernet learning self paced curriculum for deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "fast motion planning for high dof robot systems using hierarchical system identification"):
  with members:
    - "deep haptic model predictive control for robot assisted dressing"
    - "preparing for the unknown learning a universal policy with online system identification"
    - "manipulating highly deformable materials using a visual feedback dictionary"
    - "proximal policy optimization algorithms"
    - "kinodynamic rrt optimal motion planning for systems with linear differential constraints"
    - "two hierarchies of spline interpolations practical algorithms for multivariate higher order splines"

Connected through hyperedge HE-3 (induced by source paper "manipulating highly deformable materials using a visual feedback dictionary"):
  with members:
    - "bandit based model selection for deformable object manipulation"

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)