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: "tractable and scalable schatten quasi norm approximations for rank minimization"
  Abstract: The Schatten quasi-norm was introduced to bridge the gap between the trace norm and rank function. However, existing algorithms are too slow or even impractical for large-scale problems. Motivated by the equivalence relation between the trace norm and its bilinear spectral penalty, we define two tractable Schatten norms, i.e.\ the bi-trace and tri-trace norms, and prove that they are in essence the Schatten-$1/2$ and $1/3$ quasi-norms, respectively. By applying the two defined Schatten quasi-norms to various rank minimization problems such as MC and RPCA, we only need to solve much smaller factor matrices. We design two efficient linearized alternating minimization algorithms to solve our problems and establish that each bounded sequence generated by our algorithms converges to a critical point. We also provide the restricted strong convexity (RSC) based and MC error bounds for our algorithms. Our experimental results verified both the efficiency and effectiveness of our algorithms compared with the state-of-the-art methods.

Connected through hyperedge HE-1 (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"
    - "direct acceleration of saga using sampled negative momentum"
    - "asvrg accelerated proximal svrg"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "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-2 (induced by source paper "factor group sparse regularization for efficient low rank matrix recovery"):
  with members:
    - "online high rank matrix completion"
    - "exactly robust kernel principal component analysis"
    - "guaranteed matrix completion via non convex factorization"
    - "concentration based guarantees for low rank matrix reconstruction"
    - "robust principal component analysis"

Connected through hyperedge HE-3 (induced by source paper "low rank tensor completion for multiway visual data"):
  with members:
    - "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"
    - "scalable algorithms for tractable schatten quasi norm minimization"
    - "reweighted low rank tensor completion and its applications in video recovery"

Connected through hyperedge HE-4 (induced by source paper "bilinear factor matrix norm minimization for robust pca algorithms and applications"):
  with members:
    - "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"
    - "smoothed low rank and sparse matrix recovery by iteratively reweighted least squares minimization"
    - "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-5 (induced by source paper "alternating iteratively reweighted minimization algorithms for low rank matrix factorization"):
  with members:
    - "global optimality in low rank matrix optimization"
    - "unified scalable equivalent formulations for schatten quasi norms"
    - "guaranteed matrix completion via non convex factorization"
    - "simultaneously sparse and low rank abundance matrix estimation for hyperspectral image unmixing"
    - "generalized nonconvex nonsmooth low rank minimization"

Connected through hyperedge HE-6 (induced by source paper "a fast algorithm for convolutional structured low rank matrix recovery"):
  with members:
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "structured low rank recovery of piecewise constant signals with performance guarantees"
    - "compressive sampling using annihilating filter based low rank interpolation"
    - "super resolution mri using finite rate of innovation curves"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "recovery of piecewise smooth images from few fourier samples"
    - "off the grid recovery of piecewise constant images from few fourier samples"
    - "robust spectral compressed sensing via structured matrix completion"

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)