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: "learning an approximate model predictive controller with guarantees"
  Abstract: A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction. The framework can be used for a wide class of nonlinear systems. Any standard supervised learning technique (e.g., neural networks) can be employed to approximate the MPC from samples. In order to obtain closed-loop guarantees for the learned MPC, a robust MPC design is combined with statistical learning bounds. The MPC design ensures robustness to inaccurate inputs within given bounds, and Hoeffding’s Inequality is used to validate that the learned MPC satisfies these bounds with high confidence. The result is a closed-loop statistical guarantee on stability and constraint satisfaction for the learned MPC. The proposed learning-based MPC framework is illustrated on a nonlinear benchmark problem, for which we learn a neural network controller with guarantees.

Connected through hyperedge HE-1 (induced by source paper "large scale model predictive control with neural networks and primal active sets"):
  with members:
    - "safe and near optimal policy learning for model predictive control using primal dual neural networks"
    - "search based motion planning for quadrotors using linear quadratic minimum time control"
    - "understanding deep neural networks with rectified linear units"
    - "tensorflow a system for large scale machine learning"
    - "adam a method for stochastic optimization"
    - "embedded online optimization for model predictive control at megahertz rates"
    - "a reduction of imitation learning and structured prediction to no regret online learning"

Connected through hyperedge HE-2 (induced by source paper "aren assured relu nn architecture for model predictive control of lti systems"):
  with members:
    - "fast efficient hyperparameter tuning for policy gradients"
    - "mpc inspired neural network policies for sequential decision making"
    - "taking human out of learning applications a survey on automated machine learning"
    - "end to end learning for self driving cars"
    - "understanding deep neural networks with rectified linear units"
    - "designing neural network architectures using reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "safe and fast tracking control on a robot manipulator robust mpc and neural network control"):
  with members:
    - "large scale model predictive control with neural networks and primal active sets"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "implementation of nonlinear model predictive path following control for an industrial robot"
    - "funnel libraries for real time robust feedback motion planning"

Connected through hyperedge HE-4 (induced by source paper "safe and near optimal policy learning for model predictive control using primal dual neural networks"):
  with members:
    - "adaptive mpc for autonomous lane keeping"
    - "autonomous racing using learning model predictive control"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-5 (induced by source paper "verisig verifying safety properties of hybrid systems with neural network controllers"):
  with members:
    - "a classification based approach for approximate reachability"
    - "verification for machine learning autonomy and neural networks survey"
    - "reasoning about safety of learning enabled components in autonomous cyber physical systems"
    - "formal verification of piece wise linear feed forward neural networks"
    - "output reachable set estimation and verification for multi layer neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "the power of deeper networks for expressing natural functions"
    - "compositional falsification of cyber physical systems with machine learning components"

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)