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: "riemannian motion policies"
  Abstract: This paper introduces a new mathematical object for modular motion generation called the Riemannian Motion Policy (RMP). An RMP is an acceleration field (dynamical system) coupled with a corresponding Riemannian metric defining directions of importance at each point, typically defined on a nonlinear task space. We derive operators for transforming RMPs through the network of nonlinear maps defining their task spaces and combining multiple RMPs residing at a given task space, and show that these operators optimally preserve the geometric defined by the metrics in a way strongly analogous to the covariant transformations of natural gradients. This framework enables us to bridge the literature by fusing motion policies together across differing motion generation paradigms, from Dynamical Systems and Dynamic Movement Primitives to Optimal Control and Model Predictive Control. RMPs are easy to implement and manipulate, simplify controller design, clarify a number of open questions around how to effectively combine existing techniques, and their properties of geometric consistency, for the first time, make feasible the generic application of a single smooth and reactive motion generation system across a range of robots with zero re-tuning.

Connected through hyperedge HE-1 (induced by source paper "camera to robot pose estimation from a single image"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "dpod 6d pose object detector and refiner"
    - "real time 3d traffic cone detection for autonomous driving"
    - "deep high resolution representation learning for human pose estimation"
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep object pose estimation for semantic robotic grasping of household objects"
    - "real time highly accurate robotic grasp detection using fully convolutional neural networks with high resolution images"
    - "simple baselines for human pose estimation and tracking"

Connected through hyperedge HE-2 (induced by source paper "scaling local control to large scale topological navigation"):
  with members:
    - "a behavioral approach to visual navigation with graph localization networks"
    - "deep visual mpc policy learning for navigation"
    - "bayesian relational memory for semantic visual navigation"
    - "neural autonomous navigation with riemannian motion policy"
    - "combining optimal control and learning for visual navigation in novel environments"
    - "visual memory for robust path following"
    - "zero shot visual imitation"
    - "gibson env real world perception for embodied agents"

Connected through hyperedge HE-3 (induced by source paper "riemannian motion policy fusion through learnable lyapunov function reshaping"):
  with members:
    - "stable concurrent controller composition for multi objective robotic tasks"
    - "representing robot task plans as robust logical dynamical systems"
    - "multi objective policy generation for multi robot systems using riemannian motion policies"
    - "learning latent space dynamics for tactile servoing"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "convergence of value aggregation for imitation learning"
    - "fast policy learning through imitation and reinforcement"
    - "approximately optimal continuous time motion planning and control via probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "neural autonomous navigation with riemannian motion policy"):
  with members:
    - "gibson env real world perception for embodied agents"
    - "stereo dso large scale direct sparse visual odometry with stereo cameras"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "end to end driving via conditional imitation learning"
    - "sfm net learning of structure and motion from video"
    - "cognitive mapping and planning for visual navigation"
    - "unsupervised monocular depth estimation with left right consistency"
    - "cad2rl real single image flight without a single real image"

Connected through hyperedge HE-5 (induced by source paper "dexpilot vision based teleoperation of dexterous robotic hand arm system"):
  with members:
    - "making convolutional networks shift invariant again"
    - "learning joint reconstruction of hands and manipulated objects"
    - "learning latent plans from play"
    - "generalized feedback loop for joint hand object pose estimation"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "task oriented hand motion retargeting for dexterous manipulation imitation"
    - "vision based teleoperation of shadow dexterous hand using end to end deep neural network"
    - "neural kinematic networks for unsupervised motion retargetting"

Connected through hyperedge HE-6 (induced by source paper "bayesian optimization meets riemannian manifolds in robot learning"):
  with members:
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "deep kernels for optimizing locomotion controllers"
    - "deep reinforcement learning a brief survey"
    - "bayesian optimization using domain knowledge on the atrias biped"
    - "on the design of lqr kernels for efficient controller learning"
    - "pymanopt a python toolbox for optimization on manifolds using automatic differentiation"
    - "learning dynamic robot to human object handover from human feedback"
    - "a sensorimotor reinforcement learning framework for physical human robot interaction"

Connected through hyperedge HE-7 (induced by source paper "trajectory optimization for coordinated human robot collaboration"):
  with members:
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"
    - "on the fundamental importance of gauss newton in motion optimization"
    - "newton methods for k order markov constrained motion problems"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-8 (induced by source paper "stable concurrent controller composition for multi objective robotic tasks"):
  with members:
    - "multi objective policy generation for multi robot systems using riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "the robotarium a remotely accessible swarm robotics research testbed"
    - "multi objective compositions for collision free connectivity maintenance in teams of mobile robots"

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)