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: "robust subspace system identification via weighted nuclear norm optimization"
  Abstract: Subspace identification is a classical and very well studied problem in system identification. The problem was recently posed as a convex optimization problem via the nuclear norm relaxation. Inspired by robust PCA, we extend this framework to handle outliers. The proposed framework takes the form of a convex optimization problem with an objective that trades off fit, rank and sparsity. As in robust PCA, it can be problematic to find a suitable regularization parameter. We show how the space in which a suitable parameter should be sought can be limited to a bounded open set of the two dimensional parameter space. In practice, this is very useful since it restricts the parameter space that is needed to be surveyed.

Connected through hyperedge HE-1 (induced by source paper "analyzing the weighted nuclear norm minimization and nuclear norm minimization based on group sparse representation"):
  with members:
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "group based sparse representation for image restoration"
    - "image restoration using joint statistical modeling in a space transform domain"
    - "robust recovery of subspace structures by low rank representation"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-2 (induced by source paper "a comparative study for the nuclear norms minimization methods"):
  with members:
    - "graph laplacian regularization for image denoising analysis in the continuous domain"
    - "non convex weighted lp nuclear norm based admm framework for image restoration"
    - "analyzing the group sparsity based on the rank minimization methods"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "robust pca via nonconvex rank approximation"
    - "group based sparse representation for image restoration"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"

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:
    - "bayesian and regularization approaches to multivariable linear system identification the role of rank penalties"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "robust em kernel based methods for linear system identification"):
  with members:
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-5 (induced by source paper "approximate regularization path for nuclear norm based h2 model reduction"):
  with members:
    - "rank aggregation via nuclear norm minimization"

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)