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 "combining coarse and fine physics for manipulation using parallel in time integration"):
  Abstract: We present a method for fast and accurate physics-based predictions during non-prehensile manipulation planning and control. Given an initial state and a sequence of controls, the problem of predicting the resulting sequence of states is a key component of a variety of model-based planning and control algorithms. We propose combining a coarse (i.e. computationally cheap but not very accurate) predictive physics model, with a fine (i.e. computationally expensive but accurate) predictive physics model, to generate a hybrid model that is at the required speed and accuracy for a given manipulation task. Our approach is based on the Parareal algorithm, a parallel-in-time integration method used for computing numerical solutions for general systems of ordinary differential equations. We use Parareal to combine a coarse pushing model with an off-the-shelf physics engine to deliver physics-based predictions that are as accurate as the physics engine but runs in substantially less wall-clock time, thanks to Parareal being amenable to parallelization. We use these physics-based predictions in a model-predictive-control framework based on trajectory optimization, to plan pushing actions that avoid an obstacle and reach a goal location. We show that by combining the two physics models, we can achieve the same success rates as the planner that uses the off-the-shelf physics engine directly, but significantly faster. We present experiments in simulation and on a real robotic setup.

Includes members:
  Member "efficient computation of higher order variational integrators in robotic simulation and trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "linear time variational integrators in maximal coordinates")
  Member "sim to real reinforcement learning for deformable object manipulation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "when deep learning meets data alignment a review on deep registration networks drns")
  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 "robustness via retrying closed loop robotic manipulation with self supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action for better prediction")
  Member "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "parareal with a learned coarse model for robotic manipulation")
  Member "real time online re planning for grasping under clutter and uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "guided constrained policy optimization for dynamic quadrupedal robot locomotion")

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)