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: "experimental validation of contact dynamics for in hand manipulation"
  Abstract: This paper evaluates state-of-the-art contact models at predicting the motions and forces involved in simple in-hand robotic manipulations. In particular it focuses on three primitive actions --linear sliding, pivoting, and rolling-- that involve contacts between a gripper, a rigid object, and their environment. The evaluation is done through thousands of controlled experiments designed to capture the motion of object and gripper, and all contact forces and torques at 250Hz. We demonstrate that a contact modeling approach based on Coulomb's friction law and maximum energy principle is effective at reasoning about interaction to first order, but limited for making accurate predictions. We attribute the major limitations to 1) the non-uniqueness of force resolution inherent to grasps with multiple hard contacts of complex geometries, 2) unmodeled dynamics due to contact compliance, and 3) unmodeled geometries dueto manufacturing defects.

Connected through hyperedge HE-1 (induced by source paper "let s push things forward a survey on robot pushing"):
  with members:
    - "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"
    - "feature based transfer learning for robotic push manipulation"
    - "a data efficient approach to precise and controlled pushing"
    - "learning a physical long term predictor"
    - "visual interaction networks"
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "deep predictive policy training using reinforcement learning"
    - "a probabilistic data driven model for planar pushing"

Connected through hyperedge HE-2 (induced by source paper "sampling based planning of in hand manipulation with external pushes"):
  with members:
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "combining physical simulators and object based networks for control"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "universal planning networks"
    - "graph networks as learnable physics engines for inference and control"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "a data efficient approach to precise and controlled pushing"
    - "combining learned and analytical models for predicting action effects"
    - "value prediction network"
    - "learning model based planning from scratch"

Connected through hyperedge HE-4 (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-5 (induced by source paper "estimation and exploitation of objects inertial parameters in robotic grasping and manipulation a survey"):
  with members:
    - "center of mass based grasp pose adaptation using 3d range and force torque sensing"
    - "grasp that optimises objectives along post grasp trajectories"
    - "information theoretic model identification and policy search using physics engines with application to robotic manipulation"
    - "analysis and observations from the first amazon picking challenge"
    - "a compositional object based approach to learning physical dynamics"

Connected through hyperedge HE-6 (induced by source paper "propagation networks for model based control under partial observation"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "universal planning networks"
    - "graph networks as learnable physics engines for inference and control"
    - "simple recurrent units for highly parallelizable recurrence"
    - "value prediction network"
    - "learning model based planning from scratch"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "neural message passing for quantum chemistry"

Connected through hyperedge HE-7 (induced by source paper "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "combining learned and analytical models for predicting action effects"
    - "stochastic variational video prediction"
    - "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
    - "gp sum gaussian processes filtering of non gaussian beliefs"
    - "learning data efficient rigid body contact models case study of planar impact"
    - "a probabilistic data driven model for planar pushing"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

Connected through hyperedge HE-8 (induced by source paper "in hand manipulation via motion cones"):
  with members:
    - "reactive planar manipulation with convex hybrid mpc"
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"
    - "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"

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)