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: "weighted algorithms for compressed sensing and matrix completion"
  Abstract: This paper is about iteratively reweighted basis-pursuit algorithms for compressed sensing and matrix completion problems. In a rst part, we give a theoretical explanation of the fact that reweighted basis pursuit can improve a lot upon basis pursuit for exact recovery in compressed sensing. We exhibit a condition that links the accuracy of the weights to the RIP and incoherency constants, which ensures exact recovery. In a second part, we introduce a new algorithm for matrix completion, based on the idea of iterative reweighting. Since a weighted nuclear \norm" is typically non-convex, it cannot be used easily as an objective function. So, we dene a new estimator based on a xed-point equation. We give empirical evidences of the fact that this new algorithm leads to strong improvements over nuclear norm minimization on simulated and real matrix completion problems.

Connected through hyperedge HE-1 (induced by source paper "a simple prior free method for non rigid structure from motion factorization revisited"):
  with members:
    - "jumping manifolds geometry aware dense non rigid structure from motion"
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "a benchmark and evaluation of non rigid structure from motion"
    - "scalable dense non rigid structure from motion a grassmannian perspective"
    - "monocular dense 3d reconstruction of a complex dynamic scene from two perspective frames"
    - "analyzing the weighted nuclear norm minimization and nuclear norm minimization based on group sparse representation"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-2 (induced by source paper "subspace clustering based on low rank representation and weighted nuclear norm minimization"):
  with members:
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "linearized alternating direction method with parallel splitting and adaptive penalty for separable convex programs in machine learning"
    - "robust recovery of subspace structures by low rank representation"
    - "generalized principal component analysis gpca"
    - "sparse subspace clustering algorithm theory and applications"
    - "hybrid linear modeling via local best fit flats"
    - "a geometric analysis of subspace clustering with outliers"
    - "the power of convex relaxation near optimal matrix completion"

Connected through hyperedge HE-3 (induced by source paper "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"):
  with members:
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "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"
    - "matrix completion with noise"

Connected through hyperedge HE-4 (induced by source paper "partial sum minimization of singular values in robust pca algorithm and applications"):
  with members:
    - "fast randomized singular value thresholding for nuclear norm minimization"
    - "stable principal component pursuit"
    - "tilt transform invariant low rank textures"
    - "robust principal component analysis"
    - "convex sparse matrix factorizations"

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.IT(Information Theory)