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: "approximate optimal trajectory tracking for continuous time nonlinear systems"
  Abstract: Adaptive dynamic programming has been investigated and used as a method to approximately solve optimal regulation problems. However, the extension of this technique to optimal tracking problems for continuous-time nonlinear systems has remained a non-trivial open problem. The control development in this paper guarantees ultimately bounded tracking of a desired trajectory, while also ensuring that the enacted controller approximates the optimal 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"
    - "model based reinforcement learning for approximate optimal regulation"
    - "learning continuous control policies by stochastic value gradients"
    - "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 "model based reinforcement learning in differential graphical games"):
  with members:
    - "model based reinforcement learning for infinite horizon approximate optimal tracking"
    - "model based reinforcement learning for approximate optimal regulation"
    - "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 infinite horizon approximate optimal tracking"):
  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"

Connected through hyperedge HE-5 (induced by source paper "model based reinforcement learning for approximate optimal regulation"):
  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"
    - "reinforcement learning a survey"

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)