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: "uncertainty averse pushing with model predictive path integral control"
  Abstract: Planning robust robot manipulation requires good forward models that enable robust plans to be found. This work shows how to achieve this using a forward model learned from robot data to plan push manipulations. We explore learning methods (Gaussian Process Regression, and an Ensemble of Mixture Density Networks) that give estimates of the uncertainty in their predictions. These learned models are utilised by a model predictive path integral (MPPI) controller to plan how to push the box to a goal location. The planner avoids regions of high predictive uncertainty in the forward model. This includes both inherent uncertainty in dynamics, and meta uncertainty due to limited data. Thus, pushing tasks are completed in a robust fashion with respect to estimated uncertainty in the forward model and without the need of differentiable cost functions. We demonstrate the method on a real robot, and show that learning can outperform physics simulation. Using simulation, we also show the ability to plan uncertainty averse paths.

Connected through hyperedge HE-1 (induced by source paper "disco double likelihood free inference stochastic control"):
  with members:
    - "bayessim adaptive domain randomization via probabilistic inference for robotics simulators"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "online adaptation of deep architectures with reinforcement learning"

Connected through hyperedge HE-2 (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-3 (induced by source paper "parareal with a learned coarse model for robotic manipulation"):
  with members:
    - "combining coarse and fine physics for manipulation using parallel in time integration"
    - "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization"
    - "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing"
    - "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"
    - "parallelizing over artificial neural network training runs with multigrid"

Connected through hyperedge HE-4 (induced by source paper "a model predictive approach for online mobile manipulation of nonholonomic objects using learned dynamics"):
  with members:
    - "reactive planar manipulation with convex hybrid mpc"
    - "hybrid control trajectory optimization under uncertainty"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"

Connected through hyperedge HE-5 (induced by source paper "a data efficient approach to precise and controlled pushing"):
  with members:
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "self supervised visual planning with temporal skip connections"
    - "reactive planar manipulation with convex hybrid mpc"
    - "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"
    - "feedback control of the pusher slider system a story of hybrid and underactuated contact dynamics"
    - "learning to poke by poking experiential learning of intuitive physics"

Connected through hyperedge HE-6 (induced by source paper "pushing fast and slow task adaptive mpc for pushing manipulation under uncertainty"):
  with members:
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "uncertainty aware learning from demonstration using mixture density networks with sampling free variance modeling"
    - "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"
    - "model predictive path integral control using covariance variable importance sampling"

Connected through hyperedge HE-7 (induced by source paper "real time online re planning for grasping under clutter and uncertainty"):
  with members:
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "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"
    - "model predictive path integral control using covariance variable importance sampling"

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)