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 "identification of nonlinear block oriented systems starting from linear approximations a survey"):
  Abstract: Block-oriented models are popular in nonlinear modeling because of their advantages to be quite simple to understand and easy to use. Many different identification approaches were developed over the years to estimate the parameters of a wide range of block-oriented models. One class of these approaches uses linear approximations to initialize the identification algorithm. The best linear approximation framework and the $\epsilon$-approximation framework, or equivalent frameworks, allow the user to extract important information about the system, guide the user in selecting good candidate model structures and orders, and they prove to be a good starting point for nonlinear system identification algorithms. This paper gives an overview of the different block-oriented models that can be modeled using linear approximations, and of the identification algorithms that have been developed in the past. A non-exhaustive overview of the most important other block-oriented system identification approaches is also provided throughout this paper.

Includes members:
  Member "modeling parallel wiener hammerstein systems using tensor decomposition of volterra kernels":
    also connected through other hyperedges:
      (none)
  Member "recursive discrete time models for continuous time systems under band limited assumptions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "grey box state space identification of nonlinear mechanical vibrations")
  Member "using the best linear approximation with varying excitation signals for nonlinear system characterization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinear system identification a user oriented roadmap")
  Member "a new kernel based approach to overparameterized hammerstein system identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "modeling and identification of uncertain input systems")
  Member "initial estimates for wiener hammerstein models using phase coupled multisines":
    also connected through other hyperedges:
      (none)

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)