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: "bayesian and regularization approaches to multivariable linear system identification the role of rank penalties"
  Abstract: Recent developments in linear system identification have proposed the use of non-parameteric methods, relying on regularization strategies, to handle the so-called bias/variance trade-off. This paper introduces an impulse response estimator which relies on an $\ell_2$-type regularization including a rank-penalty derived using the log-det heuristic as a smooth approximation to the rank function. This allows to account for different properties of the estimated impulse response (e.g. smoothness and stability) while also penalizing high-complexity models. This also allows to account and enforce coupling between different input-output channels in MIMO systems. According to the Bayesian paradigm, the parameters defining the relative weight of the two regularization terms as well as the structure of the rank penalty are estimated optimizing the marginal likelihood. Once these hyperameters have been estimated, the impulse response estimate is available in closed form. Experiments show that the proposed method is superior to the estimator relying on the "classic" $\ell_2$-regularization alone as well as those based in atomic and nuclear norm.

Connected through hyperedge HE-1 (induced by source paper "an empirical bayes approach to identification of modules in dynamic networks"):
  with members:
    - "optimal model order reduction with the steiglitz mcbride method for open loop data"
    - "variance analysis of linear simo models with spatially correlated noise"
    - "robust em kernel based methods for linear system identification"
    - "julia a fresh approach to numerical computing"
    - "subspace identification of large scale interconnected systems"

Connected through hyperedge HE-2 (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"
    - "regularized system identification using orthonormal basis functions"
    - "relevance singular vector machine for low rank matrix sensing"
    - "kernels for linear time invariant system identification"

Connected through hyperedge HE-3 (induced by source paper "regularized linear system identification using atomic nuclear and kernel based norms the role of the stability constraint"):
  with members:
    - "robust subspace system identification via weighted nuclear norm optimization"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "maximum entropy vector kernels for mimo system identification"):
  with members:
    - "regularization and bayesian learning in dynamical systems past present and future"

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)