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: "scalable algorithms for tractable schatten quasi norm minimization"
  Abstract: The Schatten-p quasi-norm $(0<p<1)$ is usually used to replace the standard nuclear norm in order to approximate the rank function more accurately. However, existing Schatten-p quasi-norm minimization algorithms involve singular value decomposition (SVD) or eigenvalue decomposition (EVD) in each iteration, and thus may become very slow and impractical for large-scale problems. In this paper, we first define two tractable Schatten quasi-norms, i.e., the Frobenius/nuclear hybrid and bi-nuclear quasi-norms, and then prove that they are in essence the Schatten-2/3 and 1/2 quasi-norms, respectively, which lead to the design of very efficient algorithms that only need to update two much smaller factor matrices. We also design two efficient proximal alternating linearized minimization algorithms for solving representative matrix completion problems. Finally, we provide the global convergence and performance guarantees for our algorithms, which have better convergence properties than existing algorithms. Experimental results on synthetic and real-world data show that our algorithms are more accurate than the state-of-the-art methods, and are orders of magnitude faster.

Connected through hyperedge HE-1 (induced by source paper "matrix completion with deterministic sampling theories and methods"):
  with members:
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "incoherence optimal matrix completion"
    - "robust recovery of subspace structures by low rank representation"
    - "a combinatorial algebraic approach for the identifiability of low rank matrix completion"
    - "the algebraic combinatorial approach for low rank matrix completion"
    - "restricted strong convexity and weighted matrix completion optimal bounds with noise"
    - "robust pca via outlier pursuit"

Connected through hyperedge HE-2 (induced by source paper "efficient relaxed gradient support pursuit for sparsity constrained non convex optimization"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "direct acceleration of saga using sampled negative momentum"
    - "asvrg accelerated proximal svrg"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "stochastic variance reduced optimization for nonconvex sparse learning"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "gradient hard thresholding pursuit for sparsity constrained optimization"

Connected through hyperedge HE-3 (induced by source paper "nonconvex regularization based sparse and low rank recovery in signal processing statistics and machine learning"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "efficient outlier removal for large scale global structure from motion"
    - "nonconvex regularization based sparse recovery and demixing with application to color image inpainting"
    - "deterministic approximate methods for maximum consensus robust fitting"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "enhanced low rank matrix approximation"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-4 (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-5 (induced by source paper "low rank tensor completion for multiway visual data"):
  with members:
    - "tractable and scalable schatten quasi norm approximations for rank minimization"
    - "simultaneous tensor completion and denoising by noise inequality constrained convex optimization"
    - "missing slice recovery for tensors using a low rank model in embedded space"
    - "efficient tensor completion for color image and video recovery low rank tensor train"
    - "efficient low rank tensor ring completion"
    - "low rank tensor completion by truncated nuclear norm regularization"
    - "low tubal rank tensor completion using alternating minimization"
    - "reweighted low rank tensor completion and its applications in video recovery"

Connected through hyperedge HE-6 (induced by source paper "tractable and scalable schatten quasi norm approximations for rank minimization"):
  with members:
    - "generalized nonconvex nonsmooth low rank minimization"
    - "l_ 1 2 regularization convergence of iterative half thresholding algorithm"
    - "matrix completion from a few entries"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (induced by source paper "enhanced low rank matrix approximation"):
  with members:
    - "sparsity based algorithm for detecting faults in rotating machines"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "convex denoising using non convex tight frame regularization"
    - "a note on patch based low rank minimization for fast image denoising"
    - "transformed schatten 1 iterative thresholding algorithms for low rank matrix completion"
    - "transformed schatten 1 iterative thresholding algorithms for matrix rank minimization and applications"
    - "patch based low rank minimization for image denoising"
    - "group sparse signal denoising non convex regularization convex optimization"

Connected through hyperedge HE-8 (induced by source paper "unified scalable equivalent formulations for schatten quasi norms"):
  with members:
    - "generalized higher order tensor decomposition via parallel admm"
    - "low rank matrix recovery from errors and erasures"
    - "robust pca via outlier pursuit"
    - "the power of convex relaxation near optimal matrix completion"
    - "robust principal component analysis"

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.NA(Numerical Analysis)