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: "time integrating articulated body dynamics using position based collocation method"
  Abstract: We present a new time integrator for articulated body dynamics. We formulate the governing equations of the dynamics using only the position variables and recast the position-based articulated dynamics as an optimization problem. Our reformulation allows us to integrate the dynamics in a fully implicit manner without computing high-order derivatives. Therefore, under arbitrarily large timestep sizes, the stability of our time integration scheme is guaranteed using an off-the-shelf numerical optimizer. In addition to stability, we show that, similar to the Runge-Kutta method, the accuracy of our time integrator can also be increased arbitrarily by using a high-order collocation method. We provide efficient algorithms to perform time integration using our position-based formulation. We show that each iteration of optimization has a complexity of O(N) using Quasi-Newton method or O(N^2) using Newton's method, where N is the number of links. Finally, our method is highly parallelizable and can be accelerated using a Graphics Processing Unit (GPU). We highlight the efficiency and stability of our method on different benchmarks and compare the performance with prior articulated body dynamics simulation methods based on the Newton-Euler's equation. Our method is stable under a timestep size as large as 0.1s. Using a larger timestep size and less timesteps, our method achieves up to 4 times speedup on a single-core CPU. With GPU acceleration, we observe an additional 3-6 times speedup over a 4-core CPU.

Connected through hyperedge HE-1 (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"
    - "real time online re planning for grasping under clutter and uncertainty"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "combining learned and analytical models for predicting action effects"

Connected through hyperedge HE-2 (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"
    - "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"

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)