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 probabilistic data driven model for planar pushing"
  Abstract: This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes (VHGP) that capture the mean and variance of a stochastic function. We show that we can learn accurate models that outperform analytical models after less than 100 samples and saturate in performance with less than 1000 samples. We validate the results against a collected dataset of repeated trajectories, and use the learned models to study questions such as the nature of the variability in pushing, and the validity of the quasi-static assumption.

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"
    - "information theoretic model identification and policy search using physics engines with application to robotic manipulation"

Connected through hyperedge HE-2 (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-3 (induced by source paper "combining coarse and fine physics for manipulation using parallel in time integration"):
  with members:
    - "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization"
    - "sim to real reinforcement learning for deformable object manipulation"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "time integrating articulated body dynamics using position based collocation method"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning to poke by poking experiential learning of intuitive physics"

Connected through hyperedge HE-5 (induced by source paper "learning deep bayesian latent variable regression models that generalize when non identifiability is a problem"):
  with members:
    - "mine mutual information neural estimation"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "modular meta learning in abstract graph networks for combinatorial generalization"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "theoretical impediments to machine learning with seven sparks from the causal revolution"
    - "learning quickly to plan quickly using modular meta learning"
    - "evolutionary architecture search for deep multitask networks"
    - "graph networks as learnable physics engines for inference and control"
    - "modular meta learning"
    - "learning particle dynamics for manipulating rigid bodies deformable objects and fluids"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"

Connected through hyperedge HE-7 (induced by source paper "learning synergies between pushing and grasping with self supervised deep reinforcement learning"):
  with members:
    - "data efficient deep reinforcement learning for dexterous manipulation"
    - "learning complex dexterous manipulation with deep reinforcement learning and demonstrations"
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "deep visual foresight for planning robot motion"
    - "high precision grasp pose detection in dense clutter"
    - "learning to push by grasping using multiple tasks for effective learning"
    - "multi view self supervised deep learning for 6d pose estimation in the amazon picking challenge"
    - "a convex polynomial force motion model for planar sliding identification and application"

Connected through hyperedge HE-8 (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"
    - "learning data efficient rigid body contact models case study of planar impact"
    - "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)