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: "model based reinforcement learning for approximate optimal regulation"
  Abstract: Reinforcement learning (RL)-based online approximate optimal control methods applied to deterministic systems typically require a restrictive persistence of excitation (PE) condition for convergence. This paper develops a concurrent learning (CL)-based implementation of model-based RL to solve approximate optimal regulation problems online under a PE-like rank condition. The development is based on the observation that, given a model of the system, RL can be implemented by evaluating the Bellman error at any number of desired points in the state space. In this result, a parametric system model is considered, and a CL-based parameter identifier is developed to compensate for uncertainty in the parameters. Uniformly ultimately bounded regulation of the system states to a neighborhood of the origin, and convergence of the developed policy to a neighborhood of the optimal policy are established using a Lyapunov-based analysis, and simulation results are presented to demonstrate the performance of the developed controller.

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"
    - "efficient model based reinforcement learning for approximate online optimal control"
    - "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 "model based reinforcement learning in differential graphical games"):
  with members:
    - "model based reinforcement learning for infinite horizon approximate optimal tracking"
    - "approximate optimal trajectory tracking for continuous time nonlinear systems"
    - "approximate optimal cooperative decentralized control for consensus in a topological network of agents with uncertain nonlinear dynamics"

Connected through hyperedge HE-3 (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"
    - "efficient model based reinforcement learning for approximate online optimal control"
    - "concurrent learning based approximate feedback nash equilibrium solution of n player nonzero sum differential games"

Connected through hyperedge HE-4 (induced by source paper "model based reinforcement learning for infinite horizon approximate optimal tracking"):
  with members:
    - "approximate optimal trajectory tracking for continuous time nonlinear systems"
    - "concurrent learning based approximate feedback nash equilibrium solution of n player nonzero sum differential games"

Connected through hyperedge HE-5 (induced by source paper "efficient model based reinforcement learning for approximate online optimal control"):
  with members:
    - "model based reinforcement learning for infinite horizon approximate optimal tracking"
    - "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)