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: "cautious model predictive control using gaussian process regression"
  Abstract: Gaussian process (GP) regression has been widely used in supervised machine learning due to its flexibility and inherent ability to describe uncertainty in function estimation. In the context of control, it is seeing increasing use for modeling of nonlinear dynamical systems from data, as it allows the direct assessment of residual model uncertainty. We present a model predictive control (MPC) approach that integrates a nominal system with an additive nonlinear part of the dynamics modeled as a GP. Approximation techniques for propagating the state distribution are reviewed and we describe a principled way of formulating the chance constrained MPC problem, which takes into account residual uncertainties provided by the GP model to enable cautious control. Using additional approximations for efficient computation, we finally demonstrate the approach in a simulation example, as well as in a hardware implementation for autonomous racing of remote controlled race cars, highlighting improvements with regard to both performance and safety over a nominal controller.

Connected through hyperedge HE-1 (induced by source paper "practical reinforcement learning for mpc learning from sparse objectives in under an hour on a real robot"):
  with members:
    - "amz driverless the full autonomous racing system"
    - "deep value model predictive control"
    - "data driven economic nmpc using reinforcement learning"
    - "plan online learn offline efficient learning and exploration via model based control"
    - "learning based model predictive control for safe exploration"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-2 (induced by source paper "deep learning tubes for tube mpc"):
  with members:
    - "regression via arbitrary quantile modeling"
    - "robust regression for safe exploration in control"
    - "monotonic trends in deep neural networks"
    - "a general framework for uncertainty estimation in deep learning"
    - "learning control lyapunov functions from counterexamples and demonstrations"
    - "linear model predictive safety certification for learning based control"
    - "learning based model predictive control for safe exploration"
    - "distributional reinforcement learning with quantile regression"

Connected through hyperedge HE-3 (induced by source paper "safe exploration of nonlinear dynamical systems a predictive safety filter for reinforcement learning"):
  with members:
    - "linear model predictive safety certification for learning based control"
    - "verifying controllers against adversarial examples with bayesian optimization"
    - "learning to adapt meta learning for model based control"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "constrained policy optimization"
    - "scalable synthesis of safety certificates from data with application to learning based control"

Connected through hyperedge HE-4 (induced by source paper "learn fast forget slow safe predictive learning control for systems with unknown and changing dynamics performing repetitive tasks"):
  with members:
    - "bayesian optimization with automatic prior selection for data efficient direct policy search"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"
    - "one shot learning of manipulation skills with online dynamics adaptation and neural network priors"
    - "model based reinforcement learning with parametrized physical models and optimism driven exploration"

Connected through hyperedge HE-5 (induced by source paper "adaptive mpc for iterative tasks"):
  with members:
    - "adaptive mpc with chance constraints for fir systems"
    - "adaptive mpc for autonomous lane keeping"
    - "a scenario approach for non convex control design"
    - "learning model predictive control for iterative tasks a data driven control framework"

Connected through hyperedge HE-6 (induced by source paper "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"):
  with members:
    - "learning model predictive control for iterative tasks"
    - "autonomous racing using learning model predictive control"
    - "learning model predictive control for iterative tasks a data driven control framework"
    - "path tracking of highly dynamic autonomous vehicle trajectories via iterative learning control"

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.SY(Systems and Control)