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: "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.

Connected through hyperedge HE-1 (induced by source paper "human like planning for reaching in cluttered environments"):
  with members:
    - "deepmind control suite"
    - "pushing fast and slow task adaptive planning for non prehensile manipulation under uncertainty"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "randomized physics based motion planning for grasping in cluttered and uncertain environments"
    - "learning sampling distributions for robot motion planning"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-2 (induced by source paper "parareal with a learned coarse model for robotic manipulation"):
  with members:
    - "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"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-3 (induced by source paper "accurate vision based manipulation through contact reasoning"):
  with members:
    - "let s push things forward a survey on robot pushing"
    - "combining physical simulators and object based networks for control"
    - "efficient humanoid contact planning using learned centroidal dynamics prediction"
    - "robustness via retrying closed loop robotic manipulation with self supervised learning"
    - "in hand manipulation via motion cones"
    - "self supervised visual planning with temporal skip connections"
    - "reactive planar manipulation with convex hybrid mpc"
    - "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)