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: "sampling based planning of in hand manipulation with external pushes"
  Abstract: This paper presents a sampling-based planning algorithm for in-hand manipulation of a grasped object using a series of external pushes. A high-level sampling-based planning framework, in tandem with a low-level inverse contact dynamics solver, effectively explores the space of continuous pushes with discrete pusher contact switch-overs. We model the frictional interaction between gripper, grasped object, and pusher, by discretizing complex surface/line contacts into arrays of hard frictional point contacts. The inverse dynamics problem of finding an instantaneous pusher motion that yields a desired instantaneous object motion takes the form of a mixed nonlinear complementarity problem. Building upon this dynamics solver, our planner generates a sequence of pushes that steers the object to a goal grasp. We evaluate the performance of the planner for the case of a parallel-jaw gripper manipulating different objects, both in simulation and with real experiments. Through these examples, we highlight the important properties of the planner: respecting and exploiting the hybrid dynamics of contact sticking/sliding/rolling and a sense of efficiency with respect to discrete contact switch-overs.

Connected through hyperedge HE-1 (induced by source paper "solving rubik s cube with a robot hand"):
  with members:
    - "active domain randomization"
    - "meta reinforcement learning with task embedding and shared policy"
    - "ai gas ai generating algorithms an alternate paradigm for producing general artificial intelligence"
    - "distilling policy distillation"
    - "deep dynamics models for learning dexterous manipulation"
    - "meta reinforcement learning as task inference"
    - "deceptionnet network driven domain randomization"
    - "meta sim learning to generate synthetic datasets"

Connected through hyperedge HE-2 (induced by source paper "reorienting objects in 3d space using pivoting"):
  with members:
    - "pneumatic shape shifting fingers to reorient and grasp"
    - "in hand manipulation via motion cones"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "learning dexterous in hand manipulation"):
  with members:
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "multi goal reinforcement learning challenging robotics environments and request for research"
    - "on policy learning robust to irreversible events an application to robotic in hand manipulation"
    - "distributed distributional deterministic policy gradients"
    - "robust adversarial reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "regrasping by fixtureless fixturing"):
  with members:
    - "in hand manipulation via motion cones"
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "dexterous manipulation graphs"):
  with members:
    - "closed chain manipulation of large objects by multi arm robotic systems"

Connected through hyperedge HE-6 (induced by source paper "pneumatic shape shifting fingers to reorient and grasp"):
  with members:
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"

Connected through hyperedge HE-7 (induced by source paper "realtime state estimation with tactile and visual sensing application to planar manipulation"):
  with members:
    - "a probabilistic data driven model for planar pushing"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"

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)