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: "integral concurrent learning adaptive control with parameter convergence without pe or state derivatives"
  Abstract: Concurrent learning is a recently developed adaptive update scheme that can be used to guarantee parameter convergence without requiring persistent excitation. However, this technique requires knowledge of state derivatives, which are usually not directly sensed and therefore must be estimated. A novel integral concurrent learning method is developed in this paper that removes the need to estimate state derivatives while maintaining parameter convergence properties. A Monte Carlo simulation illustrates improved robustness to noise compared to the traditional derivative formulation.

Connected through hyperedge HE-1 (induced by source paper "sparse wide area control of power systems using data driven reinforcement learning"):
  with members:
    - "game theoretic multi agent control and network cost allocation under communication constraints"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"

Connected through hyperedge HE-2 (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"
    - "efficient model based reinforcement learning for approximate online optimal control"

Connected through hyperedge HE-3 (induced by source paper "observer side parameter estimation for adaptive control"):
  with members:
    - "simultaneous state and parameter estimation for second order nonlinear systems"
    - "a contraction theory based analysis of the stability of the extended kalman filter"

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)