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: "data augmented contact model for rigid body simulation"
  Abstract: Accurately modeling contact behaviors for real-world, near-rigid materials remains a grand challenge for existing rigid-body physics simulators. This paper introduces a data-augmented contact model that incorporates analytical solutions with observed data to predict the contact impulse between a particular pair of objects (e.g. a specific robot foot contacting a specific surface). The method enhances the expressiveness of the standard Coulomb contact model by learning the contact behaviors from the observed data, while preserving the fundamental contact constraints whenever possible. For example, a classifier is trained to approximate the transitions between static and dynamic frictions, while non-penetration constraint during collision is enforced analytically. Our method computes the aggregated effect of contact at the rigid-body level, removing the exponential dependency on the number of contact points in many exiting contact handling algorithms.

Connected through hyperedge HE-1 (induced by source paper "tossingbot learning to throw arbitrary objects with residual physics"):
  with members:
    - "residual policy learning"
    - "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"
    - "learning data efficient rigid body contact models case study of planar impact"

Connected through hyperedge HE-2 (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"
    - "sim to real learning agile locomotion for quadruped robots"
    - "learning data efficient rigid body contact models case study of planar impact"
    - "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-3 (induced by source paper "three dimensional reconstruction of botanical trees with simulatable geometry"):
  with members:
    - "seeing tree structure from vibration"
    - "iterative deep learning for road topology extraction"
    - "combining learned and analytical models for predicting action effects"
    - "beyond the pixel wise loss for topology aware delineation"
    - "joint 2d 3d semantic data for indoor scene understanding"
    - "perceptual losses for real time style transfer and super resolution"
    - "u net convolutional networks for biomedical image segmentation"

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)