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: "fast matrix completion without the condition number"
  Abstract: We give the first algorithm for Matrix Completion whose running time and sample complexity is polynomial in the rank of the unknown target matrix, linear in the dimension of the matrix, and logarithmic in the condition number of the matrix. To the best of our knowledge, all previous algorithms either incurred a quadratic dependence on the condition number of the unknown matrix or a quadratic dependence on the dimension of the matrix in the running time. Our algorithm is based on a novel extension of Alternating Minimization which we show has theoretical guarantees under standard assumptions even in the presence of noise.

Connected through hyperedge HE-1 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-2 (induced by source paper "provable subspace tracking from missing data and matrix completion"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "subspace estimation from incomplete observations a high dimensional analysis"
    - "static and dynamic robust pca and matrix completion a review"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-3 (induced by source paper "the sparse reverse of principal component analysis for fast low rank matrix completion"):
  with members:
    - "fast low rank bayesian matrix completion with hierarchical gaussian prior models"
    - "guaranteed matrix completion via non convex factorization"
    - "online matrix completion and online robust pca"
    - "rank aggregation via nuclear norm minimization"
    - "strongly convex programming for exact matrix completion and robust principal component analysis"
    - "the power of convex relaxation near optimal matrix completion"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-4 (induced by source paper "static and dynamic robust pca and matrix completion a review"):
  with members:
    - "provable dynamic robust pca or robust subspace tracking"
    - "an overview of robust subspace recovery"
    - "video denoising via dynamic video layering"
    - "robust subspace learning robust pca robust subspace tracking and robust subspace recovery"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-5 (induced by source paper "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "fast robust and non convex subspace recovery"
    - "nonconvex matrix factorization from rank one measurements"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "phase retrieval via linear programming fundamental limits and algorithmic improvements"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovery guarantees for one hidden layer neural networks"

Connected through hyperedge HE-6 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-7 (induced by source paper "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "non convex phase retrieval from stft measurements"
    - "a non convex blind calibration method for randomised sensing strategies"
    - "fast algorithms for robust pca via gradient descent"
    - "phase retrieval using gauss newton method"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-8 (induced by source paper "no spurious local minima in nonconvex low rank problems a unified geometric analysis"):
  with members:
    - "the non convex geometry of low rank matrix optimization"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "how to escape saddle points efficiently"
    - "matrix completion has no spurious local minimum"
    - "recovery guarantee of weighted low rank approximation via alternating minimization"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "basis learning as an algorithmic primitive"

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)