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: "real time perception meets reactive motion generation"
  Abstract: We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate models and hard-to-predict environment dynamics. We quantify the importance of continuous, real-time perception and its tight integration with reactive motion generation methods in dynamic manipulation scenarios. We compare three different systems that are instantiations of the most common architectures in the field: (i) a traditional sense-plan-act approach that is still widely used, (ii) a myopic controller that only reacts to local environment dynamics and (iii) a reactive planner that integrates feedback control and motion optimization. All architectures rely on the same components for real-time perception and reactive motion generation to allow a quantitative evaluation. We extensively evaluate the systems on a real robotic platform in four scenarios that exhibit either a challenging workspace geometry or a dynamic environment. In 333 experiments, we quantify the robustness and accuracy that is due to integrating real-time feedback at different time scales in a reactive motion generation system. We also report on the lessons learned for system building.

Connected through hyperedge HE-1 (induced by source paper "6 pack category level 6d pose tracker with anchor based keypoints"):
  with members:
    - "deepim deep iterative matching for 6d pose estimation"
    - "poserbpf a rao blackwellized particle filter for 6d object pose tracking"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "cornernet lite efficient keypoint based object detection"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "densefusion 6d object pose estimation by iterative dense fusion"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "open3d a modern library for 3d data processing"

Connected through hyperedge HE-2 (induced by source paper "kpam sc generalizable manipulation planning using keypoint affordance and shape completion"):
  with members:
    - "inferring occluded geometry improves performance when retrieving an object from dense clutter"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "object placement planning and optimization for robot manipulators"
    - "robust grasp planning over uncertain shape completions"
    - "kpam keypoint affordances for category level robotic manipulation"
    - "normalized object coordinate space for category level 6d object pose and size estimation"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "category level 6d object pose recovery in depth images"

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"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "convergence of value aggregation for imitation learning"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-4 (induced by source paper "learning arbitration for shared autonomy by hindsight data aggregation"):
  with members:
    - "a blended human robot shared control framework to handle drift and latency"
    - "shared autonomy via deep reinforcement learning"
    - "shared autonomy via hindsight optimization for teleoperation and teaming"
    - "human robot mutual adaptation in shared autonomy"
    - "goal set inverse optimal control and iterative re planning for predicting human reaching motions in shared workspaces"

Connected through hyperedge HE-5 (induced by source paper "teleoperator imitation with continuous time safety"):
  with members:
    - "learning contracting vector fields for stable imitation learning"
    - "deep imitation learning for complex manipulation tasks from virtual reality teleoperation"
    - "control design along trajectories with sums of squares programming"

Connected through hyperedge HE-6 (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"
    - "improved gelsight tactile sensor for measuring geometry and slip"

Connected through hyperedge HE-7 (induced by source paper "rmpflow a computational graph for automatic motion policy generation"):
  with members:
    - "riemannian motion policies"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "approximately optimal continuous time motion planning and control via probabilistic inference"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "sampling based algorithms for optimal motion planning"

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)