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: "nonconvex optimization meets low rank matrix factorization an overview"
  Abstract: Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minima, simple iterative methods such as gradient descent have been remarkably successful in practice. The theoretical footings, however, had been largely lacking until recently. In this tutorial-style overview, we highlight the important role of statistical models in enabling efficient nonconvex optimization with performance guarantees. We review two contrasting approaches: (1) two-stage algorithms, which consist of a tailored initialization step followed by successive refinement; and (2) global landscape analysis and initialization-free algorithms. Several canonical matrix factorization problems are discussed, including but not limited to matrix sensing, phase retrieval, matrix completion, blind deconvolution, and robust principal component analysis. Special care is taken to illustrate the key technical insights underlying their analyses. This article serves as a testament that the integrated consideration of optimization and statistics leads to fruitful research findings.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "hyperplane clustering via dual principal component pursuit"

Connected through hyperedge HE-2 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval"):
  with members:
    - "optimal spectral initialization for signal recovery with applications to phase retrieval"
    - "linear spectral estimators and an application to phase retrieval"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "non convex phase retrieval from stft measurements"
    - "phasemax convex phase retrieval via basis pursuit"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "an elementary proof of convex phase retrieval in the natural parameter space via the linear program phasemax"

Connected through hyperedge HE-3 (induced by source paper "complete dictionary learning via ell_p norm maximization"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "exact recovery of sparsely used dictionaries"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "variational optimization on lie groups with examples of leading generalized eigenvalue problems"):
  with members:
    - "stochastic modified equations and dynamics of stochastic gradient algorithms i mathematical foundations"
    - "stochastic approximation for canonical correlation analysis"
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "an overview of gradient descent optimization algorithms"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-5 (induced by source paper "nonconvex low rank symmetric tensor completion from noisy data"):
  with members:
    - "iterative collaborative filtering for sparse noisy tensor estimation"
    - "tensor robust principal component analysis exact recovery of corrupted low rank tensors via convex optimization"
    - "fundamental conditions for low cp rank tensor completion"
    - "exact tensor completion with sum of squares"
    - "on the optimization landscape of tensor decompositions"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast algorithms for robust pca via gradient descent"
    - "low rank tensor recovery via iterative hard thresholding"

Connected through hyperedge HE-6 (induced by source paper "provable low rank phase retrieval and compressive pca"):
  with members:
    - "provable subspace tracking from missing data and matrix completion"
    - "phaseless pca low rank matrix recovery from column wise phaseless measurements"
    - "provable dynamic robust pca or robust subspace tracking"
    - "low rank phase retrieval"
    - "nearly optimal robust subspace tracking"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "sparse phase retrieval via truncated amplitude flow"
    - "fast algorithms for robust pca via gradient descent"

Connected through hyperedge HE-7 (induced by source paper "nearly minimal over parametrization of shallow neural networks"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "trainability and data dependent initialization of over parameterized relu neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"

Connected through hyperedge HE-8 (induced by source paper "implicit regularization in deep matrix factorization"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "width provably matters in optimization for deep linear neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "exploring generalization in deep learning"

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)