human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "parareal with a learned coarse model for robotic manipulation"):
  Abstract: A key component of many robotics model-based planning and control algorithms is physics predictions, that is, forecasting a sequence of states given an initial state and a sequence of controls. This process is slow and a major computational bottleneck for robotics planning algorithms. Parallel-in-time integration methods can help to leverage parallel computing to accelerate physics predictions and thus planning. The Parareal algorithm iterates between a coarse serial integrator and a fine parallel integrator. A key challenge is to devise a coarse level model that is computationally cheap but accurate enough for Parareal to converge quickly. Here, we investigate the use of a deep neural network physics model as a coarse model for Parareal in the context of robotic manipulation. In simulated experiments using the physics engine Mujoco as fine propagator we show that the learned coarse model leads to faster Parareal convergence than a coarse physics-based model. We further show that the learned coarse model allows to apply Parareal to scenarios with multiple objects, where the physics-based coarse model is not applicable. Finally, We conduct experiments on a real robot and show that Parareal predictions are close to real-world physics predictions for robotic pushing of multiple objects. Some real robot manipulation plans using Parareal can be found at this https URL .

Includes members:
  Member "combining coarse and fine physics for manipulation using parallel in time integration":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "human like planning for reaching in cluttered environments")
  Member "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "linear time variational integrators in maximal coordinates")
  Member "augmenting physical simulators with stochastic neural networks case study of planar pushing and bouncing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting the physical dynamics of unseen 3d objects")
  Member "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "human like planning for reaching in cluttered environments")
  Member "time integrating articulated body dynamics using position based collocation method":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "combining coarse and fine physics for manipulation using parallel in time integration")
  Member "real time online re planning for grasping under clutter and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "human like planning for reaching in cluttered environments")
  Member "whole body nonlinear model predictive control through contacts for quadrupeds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "guided constrained policy optimization for dynamic quadrupedal robot locomotion")
  Member "parallelizing over artificial neural network training runs with multigrid":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilevel artificial neural network training for spatially correlated learning")

Question: Please tell me which class the source paper 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)