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: "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
  Abstract: This paper presents a general framework for solving the low-rank and/or sparse matrix minimization problems, which may involve multiple nonsmooth terms. The iteratively reweighted least squares (IRLSs) method is a fast solver, which smooths the objective function and minimizes it by alternately updating the variables and their weights. However, the traditional IRLS can only solve a sparse only or low rank only minimization problem with squared loss or an affine constraint. This paper generalizes IRLS to solve joint/mixed low-rank and sparse minimization problems, which are essential formulations for many tasks. As a concrete example, we solve the Schatten-   $p$    norm and    $\ell _{2,q}$   -norm regularized low-rank representation problem by IRLS, and theoretically prove that the derived solution is a stationary point (globally optimal if    $p,q\!\!\!\geq \!\!\!1$   ). Our convergence proof of IRLS is more general than previous one that depends on the special properties of the Schatten-   $p$    norm and    $\ell _{2,q}$   -norm. Extensive experiments on both synthetic and real data sets demonstrate that our IRLS is much more efficient.

Connected through hyperedge HE-1 (induced by source paper "a group norm regularized lrr factorization model for spectral clustering"):
  with members:
    - "robust recovery of subspace structures by low rank representation"
    - "an alternating direction algorithm for matrix completion with nonnegative factors"
    - "analysis and improvement of low rank representation for subspace segmentation"

Connected through hyperedge HE-2 (induced by source paper "a comprehensive survey for low rank regularization"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "side information for face completion a robust pca approach"
    - "fast and accurate tensor completion with tensor trains a system identification approach"
    - "discriminative block diagonal representation learning for image recognition"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "large scale low rank matrix learning with nonconvex regularizers"

Connected through hyperedge HE-3 (induced by source paper "bilinear factor matrix norm minimization for robust pca algorithms and applications"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "guaranteed matrix completion via non convex factorization"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "robust principal component analysis on graphs"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust recovery of subspace structures by low rank representation"
    - "sparse subspace clustering algorithm theory and applications"

Connected through hyperedge HE-4 (induced by source paper "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"):
  with members:
    - "correntropy induced l2 graph for robust subspace clustering"
    - "correlation adaptive subspace segmentation by trace lasso"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "robust and efficient subspace segmentation via least squares regression"
    - "generalized singular value thresholding"
    - "robust recovery of subspace structures by low rank representation"
    - "weighted algorithms for compressed sensing and matrix completion"
    - "matrix completion with noise"

Connected through hyperedge HE-5 (induced by source paper "piece wise quadratic approximations of arbitrary error functions for fast and robust machine learning"):
  with members:
    - "formal proofs for nonlinear optimization"
    - "vidaexpert user friendly tool for nonlinear visualization and analysis of multidimensional vectorial data"
    - "principal manifolds and graphs in practice from molecular biology to dynamical systems"
    - "principal graphs and manifolds"
    - "robust principal component analysis"
    - "topological grammars for data approximation"

Connected through hyperedge HE-6 (induced by source paper "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "can dmd obtain a scene background in color"
    - "efficient background modeling based on sparse representation and outlier iterative removal"
    - "fast algorithms for robust pca via gradient descent"
    - "fast robust pca on graphs"
    - "approximated robust principal component analysis for improved general scene background subtraction"
    - "mpi faun an mpi based framework for alternating updating nonnegative matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "robust dictionary based data representation"):
  with members:
    - "robust and efficient subspace segmentation via least squares regression"
    - "robust recovery of subspace structures by low rank representation"
    - "a complete analysis of the l_1 p group lasso"
    - "sparse subspace clustering algorithm theory and applications"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

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.LG(Machine Learning)