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: "using the best linear approximation with varying excitation signals for nonlinear system characterization"
  Abstract: Block-oriented model structure detection is quite desirable since it helps to imagine the system with real physical elements. In this paper, we explore experimental methods to detect the internal structure of the system, using a black box approach. Two different strategies are compared and the best combination of these is introduced. The methods are applied on two real systems with a static nonlinear block in the feedback path. The main goal is to excite the system in a way that reduces the total distortion in the measured frequency response functions to have more precise measurements and more reliable decision about the structure of the system.

Connected through hyperedge HE-1 (induced by source paper "nonlinear system identification a user oriented roadmap"):
  with members:
    - "extending the best linear approximation framework to the process noise case"
    - "data driven nonlinear identification of li ion battery based on a frequency domain nonparametric analysis"
    - "regularized nonparametric volterra kernel estimation"
    - "linear system identification in a nonlinear setting nonparametric analysis of the nonlinear distortions and their impact on the best linear approximation"
    - "grey box nonlinear state space modelling for mechanical vibrations identification"
    - "parametric identification of parallel wiener hammerstein systems"

Connected through hyperedge HE-2 (induced by source paper "identification of nonlinear block oriented systems starting from linear approximations a survey"):
  with members:
    - "modeling parallel wiener hammerstein systems using tensor decomposition of volterra kernels"
    - "recursive discrete time models for continuous time systems under band limited assumptions"
    - "a new kernel based approach to overparameterized hammerstein system identification"
    - "initial estimates for wiener hammerstein models using phase coupled multisines"

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)