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: "a fast stochastic contact model for planar pushing and grasping theory and experimental validation"
  Abstract: Based on the convex force-motion polynomial model for quasi-static sliding, we derive the kinematic contact model to determine the contact modes and instantaneous object motion on a supporting surface given a position controlled manipulator. The inherently stochastic object-to-surface friction distribution is modelled by sampling physically consistent parameters from appropriate distributions, with only one parameter to control the amount of noise. Thanks to the high fidelity and smoothness of convex polynomial models, the mechanics of patch contact is captured while being computationally efficient without mode selection at support points. The motion equations for both single and multiple frictional contacts are given. Simulation based on the model is validated with robotic pushing and grasping experiments.

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"
    - "deep predictive policy training using reinforcement learning"
    - "a probabilistic data driven model for planar pushing"
    - "information theoretic model identification and policy search using physics engines with application to robotic manipulation"

Connected through hyperedge HE-2 (induced by source paper "quasi static analysis of planar sliding using friction patches"):
  with members:
    - "in hand manipulation via motion cones"
    - "stable prehensile pushing in hand manipulation with alternating sticking contacts"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"

Connected through hyperedge HE-3 (induced by source paper "non planar frictional surface contacts modeling and application to grasping"):
  with members:
    - "dense tactile force distribution estimation using gelslim and inverse fem"
    - "a convex polynomial force motion model for planar sliding identification and application"

Connected through hyperedge HE-4 (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"
    - "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"
    - "experimental validation of contact dynamics for in hand manipulation"
    - "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 "feature based transfer learning for robotic push manipulation"):
  with members:
    - "a probabilistic data driven model for planar pushing"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning to poke by poking experiential learning of intuitive physics"

Connected through hyperedge HE-6 (induced by source paper "uncertainty averse pushing with model predictive path integral control"):
  with members:
    - "a probabilistic data driven model for planar pushing"
    - "unsupervised learning for physical interaction through video prediction"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "learning to poke by poking experiential learning of intuitive physics"
    - "path integral guided policy search"
    - "model predictive path integral control using covariance variable importance sampling"
    - "adam a method for stochastic optimization"
    - "generating sequences with recurrent neural networks"

Connected through hyperedge HE-7 (induced by source paper "stable prehensile pushing in hand manipulation with alternating sticking contacts"):
  with members:
    - "experimental validation of contact dynamics for in hand manipulation"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"
    - "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)