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: "linear model predictive safety certification for learning based control"
  Abstract: While it has been repeatedly shown that learning-based controllers can provide superior performance, they often lack of safety guarantees. This paper aims at addressing this problem by introducing a model predictive safety certification (MPSC) scheme for polytopic linear systems with additive disturbances. The scheme verifies safety of a proposed learning-based input and modifies it as little as necessary in order to keep the system within a given set of constraints. Safety is thereby related to the existence of a model predictive controller (MPC) providing a feasible trajectory towards a safe target set. A robust MPC formulation accounts for the fact that the model is generally uncertain in the context of learning, which allows proving constraint satisfaction at all times under the proposed MPSC strategy. The MPSC scheme can be used in order to expand any potentially conservative set of safe states for learning and we prove an iterative technique for enlarging the safe set. Finally, a practical data-based design procedure for MPSC is proposed using scenario optimization.

Connected through hyperedge HE-1 (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"
    - "learning based model predictive control for safe exploration"
    - "cautious model predictive control using gaussian process regression"
    - "distributional reinforcement learning with quantile regression"

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"
    - "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"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "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 "safe exploration of nonlinear dynamical systems a predictive safety filter for reinforcement learning"):
  with members:
    - "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"
    - "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 "learning based model predictive control for safe exploration"):
  with members:
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "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)