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: "in hand manipulation via motion cones"
  Abstract: In this paper, we present the mechanics and algorithms to compute the set of feasible motions of an object pushed in a plane. This set is known as the motion cone and was previously described for non-prehensile manipulation tasks in the horizontal plane. We generalize its geometric construction to a broader set of planar tasks, where external forces such as gravity influence the dynamics of pushing, and prehensile tasks, where there are complex interactions between the gripper, object, and pusher. We show that the motion cone is defined by a set of low-curvature surfaces and provide a polyhedral cone approximation to it. We verify its validity with 2000 pushing experiments recorded with motion tracking system. Motion cones abstract the algebra involved in simulating frictional pushing by providing bounds on the set of feasible motions and by characterizing which pushes will stick or slip. We demonstrate their use for the dynamic propagation step in a sampling-based planning algorithm for in-hand manipulation. The planner generates trajectories that involve sequences of continuous pushes with 5-1000x speed improvements to equivalent algorithms. #R##N#Video Summary -- this https URL

Connected through hyperedge HE-1 (induced by source paper "benchmarking in hand manipulation"):
  with members:
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation graphs"
    - "pneumatic shape shifting fingers to reorient and grasp"
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "geometric in hand regrasp planning alternating optimization of finger gaits and in grasp manipulation"
    - "reinforcement learning for non prehensile manipulation transfer from simulation to physical system"
    - "reinforcement learning for pivoting task"
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"

Connected through hyperedge HE-2 (induced by source paper "tactile dexterity manipulation primitives with tactile feedback"):
  with members:
    - "tactile mapping and localization from high resolution tactile imprints"
    - "manipulation by feel touch based control with deep predictive models"
    - "maintaining grasps within slipping bound by monitoring incipient slip"
    - "in hand object stabilization by independent finger control"
    - "simultaneous contact gait and motion planning for robust multi legged locomotion via mixed integer convex optimization"
    - "reactive planar manipulation with convex hybrid mpc"
    - "analysis and observations from the first amazon picking challenge"

Connected through hyperedge HE-3 (induced by source paper "deep dynamics models for learning dexterous manipulation"):
  with members:
    - "exploring model based planning with policy networks"
    - "when to trust your model model based policy optimization"
    - "calibrated model based deep reinforcement learning"
    - "discovery of latent 3d keypoints via end to end geometric reasoning"
    - "model ensemble trust region policy optimization"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "plan online learn offline efficient learning and exploration via model based control"

Connected through hyperedge HE-4 (induced by source paper "accurate vision based manipulation through contact reasoning"):
  with members:
    - "let s push things forward a survey on robot pushing"
    - "combining physical simulators and object based networks for control"
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "efficient humanoid contact planning using learned centroidal dynamics prediction"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "self supervised visual planning with temporal skip connections"
    - "reactive planar manipulation with convex hybrid mpc"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

Connected through hyperedge HE-5 (induced by source paper "learning to solve a rubik s cube with a dexterous hand"):
  with members:
    - "houseexpo a large scale 2d indoor layout dataset for learning based algorithms on mobile robots"
    - "learning dexterous in hand manipulation"
    - "learning to interrupt a hierarchical deep reinforcement learning framework for efficient exploration"
    - "solving the rubik s cube without human knowledge"
    - "distributed distributional deterministic policy gradients"
    - "hindsight experience replay"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "learning and transfer of modulated locomotor controllers"

Connected through hyperedge HE-6 (induced by source paper "local trajectory stabilization for dexterous manipulation via piecewise affine approximations"):
  with members:
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "controller synthesis for discrete time hybrid polynomial systems via occupation measures"
    - "sampling based polytopic trees for approximate optimal control of piecewise affine systems"
    - "learning dexterous in hand manipulation"
    - "reactive planar manipulation with convex hybrid mpc"
    - "deep visual foresight for planning robot motion"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-7 (induced by source paper "dual arm in hand manipulation and regrasping using dexterous manipulation graphs"):
  with members:
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation graphs"
    - "pneumatic shape shifting fingers to reorient and grasp"
    - "preparatory manipulation planning using automatically determined single and dual arms"
    - "reinforcement learning for pivoting task"
    - "closed chain manipulation of large objects by multi arm robotic systems"
    - "developing and comparing single arm and dual arm regrasp"
    - "data driven grasp synthesis a survey"

Connected through hyperedge HE-8 (induced by source paper "reorienting objects in 3d space using pivoting"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "pneumatic shape shifting fingers to reorient and grasp"
    - "analyzing the utility of a support pin in sequential robotic manipulation"
    - "supersizing self supervision learning to grasp from 50k tries and 700 robot hours"
    - "end to end training of deep visuomotor policies"

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)