human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "nonlinear system identification a user oriented roadmap"):
  Abstract: The goal of this article is twofold. Firstly, nonlinear system identification is introduced to a wide audience, guiding practicing engineers and newcomers in the field to a sound solution of their data driven modeling problems for nonlinear dynamic systems. In addition, the article also provides a broad perspective on the topic to researchers that are already familiar with the linear system identification theory, showing the similarities and differences between the linear and nonlinear problem. The reader will be referred to the existing literature for detailed mathematical explanations and formal proofs. Here the focus is on the basic philosophy, giving an intuitive understanding of the problems and the solutions, by making a guided tour along the wide range of user choices in nonlinear system identification. Guidelines will be given in addition to many examples, to reach that goal.

Includes members:
  Member "extending the best linear approximation framework to the process noise case":
    also connected through other hyperedges:
      (none)
  Member "data driven nonlinear identification of li ion battery based on a frequency domain nonparametric analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a local polynomial approach to nonparametric estimation of the best linear approximation of lithium ion battery from multiple datasets")
  Member "regularized nonparametric volterra kernel estimation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient multidimensional regularization for volterra series estimation")
  Member "linear system identification in a nonlinear setting nonparametric analysis of the nonlinear distortions and their impact on the best linear approximation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the use of machine learning and big five personality taxonomy to predict construction workers safety behaviour")
  Member "using the best linear approximation with varying excitation signals for nonlinear system characterization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "identification of nonlinear block oriented systems starting from linear approximations a survey")
  Member "grey box nonlinear state space modelling for mechanical vibrations identification":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "grey box state space identification of nonlinear mechanical vibrations")
  Member "parametric identification of parallel wiener hammerstein systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non linear system modelling based on constrained volterra series estimates")

Question: Please tell me which class the source paper 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)