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 infinite horizon approximate optimal tracking"
  Abstract: This brief paper provides an approximate online adaptive solution to the infinite-horizon optimal tracking problem for control-affine continuous-time nonlinear systems with unknown drift dynamics. To relax the persistence of excitation condition, model-based reinforcement learning is implemented using a concurrent-learning-based system identifier to simulate experience by evaluating the Bellman error over unexplored areas of the state space. Tracking of the desired trajectory and convergence of the developed policy to a neighborhood of the optimal policy are established via Lyapunov-based stability analysis. Simulation results demonstrate the effectiveness of the developed technique.

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"
    - "simultaneous state and parameter estimation for second order nonlinear systems"
    - "efficient model based reinforcement learning for approximate online optimal control"
    - "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 "multi pseudo q learning based deterministic policy gradient for tracking control of autonomous underwater vehicles"):
  with members:
    - "control of a quadrotor with reinforcement learning"
    - "virtual to real deep reinforcement learning continuous control of mobile robots for mapless navigation"
    - "continuous control with deep reinforcement learning"
    - "deep reinforcement learning with double q learning"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "model based reinforcement learning in differential graphical games"):
  with members:
    - "model based reinforcement learning for approximate optimal regulation"
    - "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-4 (induced by source paper "model based reinforcement learning for approximate optimal regulation"):
  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"
    - "reinforcement learning a survey"

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 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)