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: "concurrent learning based approximate feedback nash equilibrium solution of n player nonzero sum differential games"
  Abstract: This paper presents a concurrent learning-based actor-critic-identifier architecture to obtain an approximate feedback-Nash equilibrium solution to an infinite horizon N-player nonzero-sum differential game. The solution is obtained online for a nonlinear control-affine system with uncertain linearly parameterized drift dynamics. It is shown that under a condition milder than persistence of excitation (PE), uniformly ultimately bounded convergence of the developed control policies to the feedback-Nash equilibrium policies can be established. Simulation results are presented to demonstrate the performance of the developed technique without an added excitation signal.

Connected through hyperedge HE-1 (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"
    - "model based reinforcement learning for approximate optimal regulation"

Connected through hyperedge HE-2 (induced by source paper "model based reinforcement learning for infinite horizon approximate optimal tracking"):
  with members:
    - "model based reinforcement learning for approximate optimal regulation"
    - "approximate optimal trajectory tracking for continuous time nonlinear systems"

Connected through hyperedge HE-3 (induced by source paper "model based reinforcement learning for approximate optimal regulation"):
  with members:
    - "model based reinforcement learning for infinite horizon approximate optimal tracking"
    - "approximate optimal trajectory tracking for continuous time nonlinear systems"
    - "reinforcement learning a survey"

Connected through hyperedge HE-4 (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"
    - "model based reinforcement learning for approximate optimal regulation"

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)