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: "fundamental limitations in performance and interpretability of common planar rigid body contact models"
  Abstract: The ability to reason about and predict the outcome of contacts is paramount to the successful execution of many robot tasks. Analytical rigid-body contact models are used extensively in planning and control due to their computational efficiency and simplicity, yet despite their prevalence, little if any empirical comparison of these models has been made and it is unclear how well they approximate contact outcomes. In this paper, we first formulate a system identification approach for six commonly used contact models in the literature, and use the proposed method to find parameters for an experimental data-set of impacts. Next, we compare the models empirically, and establish a task specific upper bound on the performance of the models and the rigid-body contact model paradigm. We highlight the limitations of these models, salient failure modes, and the care that should be taken in parameter selection, which are ultimately difficult to give a physical interpretation.

Connected through hyperedge HE-1 (induced by source paper "making sense of vision and touch learning multimodal representations for contact rich tasks"):
  with members:
    - "joint inference of kinematic and force trajectories with visuo tactile sensing"
    - "from variational to deterministic autoencoders"
    - "manipulation by feel touch based control with deep predictive models"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "learning latent space dynamics for tactile servoing"
    - "realtime state estimation with tactile and visual sensing for inserting a suction held object"

Connected through hyperedge HE-2 (induced by source paper "combining physical simulators and object based networks for control"):
  with members:
    - "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"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

Connected through hyperedge HE-3 (induced by source paper "modeling and analysis of non unique behaviors in multiple frictional impacts"):
  with members:
    - "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"
    - "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-4 (induced by source paper "benchmarking simulated robotic manipulation through a real world dataset"):
  with members:
    - "quantifying the reality gap in robotic manipulation tasks"
    - "learning dexterous in hand manipulation"
    - "a dataset of daily interactive 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 "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"):
  with members:
    - "learning dexterous in hand manipulation"
    - "more than a feeling learning to grasp and regrasp using vision and touch"
    - "state representation learning for control an overview"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "using simulation and domain adaptation to improve efficiency of deep robotic grasping"
    - "learning to represent haptic feedback for partially observable tasks"
    - "stochastic variational video prediction"
    - "deep multimodal representation learning from temporal data"

Connected through hyperedge HE-6 (induced by source paper "propagation networks for model based control under partial observation"):
  with members:
    - "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"
    - "metacontrol for adaptive imagination based optimization"

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:
    - "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"
    - "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-8 (induced by source paper "learning data efficient rigid body contact models case study of planar impact"):
  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"
    - "a convex polynomial force motion model for planar sliding identification and application"

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)