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: "regularization and bayesian learning in dynamical systems past present and future"
  Abstract: Regularization and Bayesian methods for system identification have been repopularized in the recent years, and proved to be competitive w.r.t. classical parametric approaches. In this paper we shall make an attempt to illustrate how the use of regularization in system identification has evolved over the years, starting from the early contributions both in the Automatic Control as well as Econometrics and Statistics literature. In particular we shall discuss some fundamental issues such as compound estimation problems and exchangeability which play and important role in regularization and Bayesian approaches, as also illustrated in early publications in Statistics. The historical and foundational issues will be given more emphasis (and space), at the expense of the more recent developments which are only briefly discussed. The main reason for such a choice is that, while the recent literature is readily available, and surveys have already been published on the subject, in the author's opinion a clear link with past work had not been completely clarified.

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"
    - "regularized system identification using orthonormal basis functions"

Connected through hyperedge HE-2 (induced by source paper "on kernel design for regularized lti system identification"):
  with members:
    - "filter based regularisation for impulse response modelling"
    - "on asymptotic properties of hyperparameter estimators for kernel based regularization methods"
    - "continuous time dc kernel a stable generalized first order spline kernel"
    - "on input design for regularized lti system identification power constrained input"
    - "maximum entropy properties of discrete time first order stable spline kernel"
    - "kernels for linear time invariant system identification"

Connected through hyperedge HE-3 (induced by source paper "maximum entropy vector kernels for mimo system identification"):
  with members:
    - "bayesian and regularization approaches to multivariable linear system identification the role of rank penalties"

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)