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 nmpc with gaussian process dynamics for autonomous miniature race cars"
  Abstract: This paper presents an adaptive high performance control method for autonomous miniature race cars. Racing dynamics are notoriously hard to model from first principles, which is addressed by means of a cautious nonlinear model predictive control (NMPC) approach that learns to improve its dynamics model from data and safely increases racing performance. The approach makes use of a Gaussian Process (GP) and takes residual model uncertainty into account through a chance constrained formulation. We present a sparse GP approximation with dynamically adjusting inducing inputs, enabling a real-time implementable controller. The formulation is demonstrated in simulations, which show significant improvement with respect to both lap time and constraint satisfaction compared to an NMPC without model learning.

Connected through hyperedge HE-1 (induced by source paper "a data efficient geometrically inspired polynomial kernel for robot inverse dynamic"):
  with members:
    - "a novel multiplicative polynomial kernel for volterra series identification"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "sample based learning model predictive control for linear uncertain systems"):
  with members:
    - "learning how to autonomously race a car a predictive control approach"
    - "feature based aggregation and deep reinforcement learning a survey and some new implementations"
    - "linear model predictive safety certification for learning based control"
    - "learning based predictive control for linear systems a unitary approach"
    - "learning based model predictive control for safe exploration"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"
    - "real time control for autonomous racing based on viability theory"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-3 (induced by source paper "adaptive trajectory planning and optimization at limits of handling"):
  with members:
    - "adaptive mpc with chance constraints for fir systems"
    - "learning based model predictive control for safe exploration"
    - "adaptive mpc for autonomous lane keeping"
    - "adaptive mpc for iterative tasks"
    - "a sequential two step algorithm for fast generation of vehicle racing trajectories"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "autonomous racing using learning model predictive control"

Connected through hyperedge HE-4 (induced by source paper "a novel geometrically inspired polynomial kernel for robot inverse dynamics"):
  with members:
    - "a novel multiplicative polynomial kernel for volterra series identification"
    - "proprioceptive robot collision detection through gaussian process regression"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (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 model predictive control using gaussian process regression"
    - "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-6 (induced by source paper "learning based model predictive control for safe exploration"):
  with members:
    - "linear model predictive safety certification for learning based control"
    - "a provably correct mpc approach to safety control of urban traffic networks"
    - "bayesian optimization with safety constraints safe and automatic parameter tuning in robotics"
    - "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes"
    - "model based reinforcement learning with parametrized physical models and optimism driven exploration"

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)