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: "learning data efficient rigid body contact models case study of planar impact"
  Abstract: In this paper we demonstrate the limitations of common rigid-body contact models used in the robotics community by comparing them to a collection of data-driven and data-reinforced models that exploit underlying structure inspired by the rigid contact paradigm. We evaluate and compare the analytical and data-driven contact models on an empirical planar impact data-set, and show that the learned models are able to outperform their analytical counterparts with a small training set.

Connected through hyperedge HE-1 (induced by source paper "fundamental limitations in performance and interpretability of common planar rigid body contact models"):
  with members:
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"

Connected through hyperedge HE-2 (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-3 (induced by source paper "tossingbot learning to throw arbitrary objects with residual physics"):
  with members:
    - "residual policy learning"
    - "data augmented contact model for rigid body simulation"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "residual reinforcement learning for robot control"
    - "combining learned and analytical models for predicting action effects"
    - "cartman the low cost cartesian manipulator that won the amazon robotics challenge"
    - "deep predictive policy training using reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "modeling and analysis of non unique behaviors in multiple frictional impacts"):
  with members:
    - "fundamental limitations in performance and interpretability of common planar rigid body contact models"
    - "a quasi static model and simulation approach for pushing grasping and jamming"
    - "data augmented contact model for rigid body simulation"
    - "sim to real learning agile locomotion for quadruped robots"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"
    - "a hybrid systems model for simple manipulation and self manipulation systems"
    - "a propagative model of simultaneous impact existence uniqueness and design consequences"

Connected through hyperedge HE-5 (induced by source paper "simple kinematic feedback enhances autonomous learning in bio inspired tendon driven systems"):
  with members:
    - "stabilizing unstable periodic orbits with delayed feedback control in act and wait fashion"
    - "learning dexterous in hand manipulation"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "emergence of locomotion behaviours in rich environments"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-6 (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"
    - "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"

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)