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: "efficient model based reinforcement learning for approximate online optimal control"
  Abstract: An infinite horizon optimal regulation problem is solved online for a deterministic control-affine nonlinear dynamical system using a state following (StaF) kernel method to approximate the value function. Unlike traditional methods that aim to approximate a function over a large compact set, the StaF kernel method aims to approximate a function in a small neighborhood of a state that travels within a compact set. Simulation results demonstrate that stability and approximate optimality of the control system can be achieved with significantly fewer basis functions than may be required for global approximation methods.

Connected through hyperedge HE-1 (induced by source paper "output feedback online optimal control for a class of nonlinear systems"):
  with members:
    - "model based reinforcement learning in differential graphical games"
    - "model based reinforcement learning for infinite horizon approximate optimal tracking"
    - "simultaneous state and parameter estimation for second order nonlinear systems"
    - "model based reinforcement learning for approximate optimal regulation"
    - "learning continuous control policies by stochastic value gradients"
    - "approximate optimal trajectory tracking for continuous time nonlinear systems"
    - "gaussian processes for sample efficient reinforcement learning with rmax like exploration"

Connected through hyperedge HE-2 (induced by source paper "verification for machine learning autonomy and neural networks survey"):
  with members:
    - "pragmatic pedagogic value alignment"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deep reinforcement learning for autonomous driving"
    - "a dual approach to scalable verification of deep networks"
    - "neural guided deductive search for real time program synthesis from examples"
    - "reachable set estimation and safety verification for piecewise linear systems with neural network controllers"
    - "reachable set estimation and verification for neural network models of nonlinear dynamic systems"
    - "testing deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "simultaneous state and parameter estimation for second order nonlinear systems"):
  with members:
    - "concurrent learning for parameter estimation using dynamic state derivative estimators"
    - "online output feedback parameter and state estimation for second order linear systems"
    - "integral concurrent learning adaptive control with parameter convergence without pe or state derivatives"

Connected through hyperedge HE-4 (induced by source paper "online output feedback parameter and state estimation for second order linear systems"):
  with members:
    - "concurrent learning for parameter estimation using dynamic state derivative estimators"
    - "model based reinforcement learning for approximate optimal regulation"
    - "concurrent learning based approximate feedback nash equilibrium solution of n player nonzero sum differential games"

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)