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: "stable prehensile pushing in hand manipulation with alternating sticking contacts"
  Abstract: This paper presents an approach to in-hand manipulation planning that exploits the mechanics of alternating sticking contact. Particularly, we consider the problem of manipulating a grasped object using external pushes for which the pusher sticks to the object. Given the physical properties of the object, frictional coefficients at contacts and a desired regrasp on the object, we propose a sampling-based planning framework that builds a pushing strategy concatenating different feasible stable pushes to achieve the desired regrasp. An efficient dynamics formulation for stable prehensile pushing allows us to plan in-hand manipulations 100-1000 times faster than our previous work which builds upon a complementarity formulation. Experimental observations for the generated plans show very close correspondence to the expected results from the planner. #R##N#Video Summary -- youtu.be/qOTKRJMx6Ho

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"
    - "in hand manipulation via motion cones"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "sampling based planning of in hand manipulation with external pushes"):
  with members:
    - "experimental validation of contact dynamics for in hand manipulation"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "quasi static analysis of planar sliding using friction patches"):
  with members:
    - "in hand manipulation via motion cones"
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

Connected through hyperedge HE-4 (induced by source paper "in hand sliding regrasp with spring sliding compliance"):
  with members:
    - "dexterous manipulation graphs"
    - "relaxed rigidity constraints kinematic trajectory optimization and collision avoidance for in grasp manipulation"
    - "in hand manipulation via motion cones"

Connected through hyperedge HE-5 (induced by source paper "qt opt scalable deep reinforcement learning for vision based robotic manipulation"):
  with members:
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "accelerated methods for deep reinforcement learning"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "distributed prioritized experience replay"
    - "deep reinforcement learning that matters"
    - "self supervised deep reinforcement learning with generalized computation graphs for robot navigation"
    - "grasp pose detection in point clouds"
    - "collective robot reinforcement learning with distributed asynchronous guided policy search"

Connected through hyperedge HE-6 (induced by source paper "in hand manipulation via motion cones"):
  with members:
    - "reactive planar manipulation with convex hybrid mpc"
    - "experimental validation of contact dynamics for in hand manipulation"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"
    - "sample based planning with volumes in configuration space"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-7 (induced by source paper "regrasping by fixtureless fixturing"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"
    - "in hand manipulation via motion cones"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-8 (induced by source paper "pneumatic shape shifting fingers to reorient and grasp"):
  with members:
    - "sampling based planning of in hand manipulation with external pushes"

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)