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: "learn fast forget slow safe predictive learning control for systems with unknown and changing dynamics performing repetitive tasks"
  Abstract: We present a control method for improved repetitive path following for a ground vehicle that is geared towards long-term operation where the operating conditions can change over time and are initially unknown. We use weighted Bayesian Linear Regression (wBLR) to model the unknown dynamics, and show how this simple model is more accurate in both its estimate of the mean behaviour and model uncertainty than Gaussian Process Regression and generalizes to novel operating conditions with little or no tuning. In addition, wBLR allows us to use fast adaptation and long-term learning in one, unified framework, to adapt quickly to new operating conditions and learn repetitive model errors over time. This comes with the added benefit of lower computational cost, longer look-ahead, and easier optimization when the model is used in a stochastic Model Predictive Controller (MPC). In order to fully capitalize on the long prediction horizons that are possible with this new approach, we use Tube MPC to reduce the growth of predicted uncertainty. We demonstrate the effectiveness of our approach in experiment on a 900\,kg ground robot showing results over 3.0\,km of driving with both physical and artificial changes to the robot's dynamics. All of our experiments are conducted using a stereo camera for localization.

Connected through hyperedge HE-1 (induced by source paper "driving reinforcement learning with models"):
  with members:
    - "lifelong federated reinforcement learning a learning architecture for navigation in cloud robotic systems"
    - "control tutored reinforcement learning an application to the herding problem"
    - "model ensemble trust region policy optimization"
    - "data driven policy transfer with imprecise perception simulation"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "a differentiable physics engine for deep learning in robotics"
    - "learning physical intuition of block towers by example"

Connected through hyperedge HE-2 (induced by source paper "neural swarm decentralized close proximity multirotor controlusing learned interactions"):
  with members:
    - "robust regression for safe exploration in control"
    - "episodic learning with control lyapunov functions for uncertain robotic systems"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "control regularization for reduced variance reinforcement learning"
    - "neural lander stable drone landing control using learned dynamics"
    - "residual reinforcement learning for robot control"
    - "deep sets"
    - "smooth imitation learning for online sequence prediction"

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)