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: "regularized system identification using orthonormal basis functions"
  Abstract: Most of existing results on regularized system identification focus on regularized impulse response estimation. Since the impulse response model is a special case of orthonormal basis functions, it is interesting to consider if it is possible to tackle the regularized system identification using more compact orthonormal basis functions. In this paper, we explore two possibilities. First, we construct reproducing kernel Hilbert space of impulse responses by orthonormal basis functions and then use the induced reproducing kernel for the regularized impulse response estimation. Second, we extend the regularization method from impulse response estimation to the more general orthonormal basis functions estimation. For both cases, the poles of the basis functions are treated as hyperparameters and estimated by empirical Bayes method. Then we further show that the former is a special case of the latter, and more specifically, the former is equivalent to ridge regression of the coefficients of the orthonormal basis functions.

Connected through hyperedge HE-1 (induced by source paper "on input design for regularized lti system identification power constrained input"):
  with members:
    - "filter based regularisation for impulse response modelling"
    - "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"
    - "on kernel design for regularized lti system identification"
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "regularization and bayesian learning in dynamical systems past present and future"

Connected through hyperedge HE-2 (induced by source paper "filter based regularisation for impulse response modelling"):
  with members:
    - "maximum entropy properties of discrete time first order stable spline kernel"

Connected through hyperedge HE-3 (induced by source paper "regularization and bayesian learning in dynamical systems past present and future"):
  with members:
    - "n2sid nuclear norm subsystem identification"
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "approximate regularization paths for nuclear norm minimization using singular value bounds with implementation and extended appendix"
    - "bayesian and regularization approaches to multivariable linear system identification the role of rank penalties"
    - "relevance singular vector machine for low rank matrix sensing"
    - "kernels for linear time invariant system identification"

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)