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 convex polynomial force motion model for planar sliding identification and application"
  Abstract: We propose a polynomial force-motion model for planar sliding. The set of generalized friction loads is the 1-sublevel set of a polynomial whose gradient directions correspond to generalized velocities. Additionally, the polynomial is confined to be convex even-degree homogeneous in order to obey the maximum work inequality, symmetry, shape invariance in scale, and fast invertibility. We present a simple and statistically-efficient model identification procedure using a sum-of-squares convex relaxation. Simulation and robotic experiments validate the accuracy and efficiency of our approach. We also show practical applications of our model including stable pushing of objects and free sliding dynamic simulations.

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 "learning to singulate objects using a push proposal network"):
  with members:
    - "dex net 2 0 deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics"
    - "metric learning for generalizing spatial relations to new objects"
    - "interactive perception leveraging action in perception and perception in action"
    - "fast model identification via physics engines for data efficient policy search"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "comparing human centric and robot centric sampling for robot deep learning from demonstrations"
    - "deep visual foresight for planning robot motion"
    - "high precision grasp pose detection in dense clutter"

Connected through hyperedge HE-3 (induced by source paper "omnipush accurate diverse real world dataset of pushing dynamics with rgb d video"):
  with members:
    - "meta reinforcement learning as task inference"
    - "attentive neural processes"
    - "conditional neural processes"
    - "stochastic adversarial video prediction"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "the perception distortion tradeoff"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "stochastic video generation with a learned prior"

Connected through hyperedge HE-4 (induced by source paper "object centric forward modeling for model predictive control"):
  with members:
    - "densephysnet learning dense physical object representations via multi step dynamic interactions"
    - "compositional video prediction"
    - "reasoning about physical interactions with object oriented prediction and planning"
    - "learning latent dynamics for planning from pixels"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "relational inductive biases deep learning and graph networks"
    - "self supervised visual planning with temporal skip connections"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-5 (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-6 (induced by source paper "online learning in planar pushing with combined prediction model"):
  with members:
    - "improvisation through physical understanding using novel objects as tools with visual foresight"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "deep online learning via meta learning continual adaptation for model based rl"
    - "combining learned and analytical models for predicting action effects"
    - "a probabilistic data driven model for planar pushing"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "unsupervised learning for physical interaction through video prediction"
    - "se3 nets learning rigid body motion using deep neural networks"

Connected through hyperedge HE-7 (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"
    - "in hand manipulation via motion cones"
    - "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"

Connected through hyperedge HE-8 (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 fast stochastic contact model for planar pushing and grasping theory and experimental validation"

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)