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: "structured low rank matrix factorization global optimality algorithms and applications"
  Abstract: Recently, convex formulations of low-rank matrix factorization problems have received considerable attention in machine learning. However, such formulations often require solving for a matrix of the size of the data matrix, making it challenging to apply them to large scale datasets. Moreover, in many applications the data can display structures beyond simply being low-rank, e.g., images and videos present complex spatio-temporal structures that are largely ignored by standard low-rank methods. In this paper we study a matrix factorization technique that is suitable for large datasets and captures additional structure in the factors by using a particular form of regularization that includes well-known regularizers such as total variation and the nuclear norm as particular cases. Although the resulting optimization problem is non-convex, we show that if the size of the factors is large enough, under certain conditions, any local minimizer for the factors yields a global minimizer. A few practical algorithms are also provided to solve the matrix factorization problem, and bounds on the distance from a given approximate solution of the optimization problem to the global optimum are derived. Examples in neural calcium imaging video segmentation and hyperspectral compressed recovery show the advantages of our approach on high-dimensional datasets.

Connected through hyperedge HE-1 (induced by source paper "clustering with similarity preserving"):
  with members:
    - "robust graph learning from noisy data"
    - "low rank kernel learning for graph based clustering"
    - "similarity learning via kernel preserving embedding"
    - "twin learning for similarity and clustering a unified kernel approach"
    - "unified spectral clustering with optimal graph"
    - "a comprehensive survey of graph embedding problems techniques and applications"
    - "kernel spectral clustering and applications"
    - "towards k means friendly spaces simultaneous deep learning and clustering"

Connected through hyperedge HE-2 (induced by source paper "binary component decomposition part i the positive semidefinite case"):
  with members:
    - "on the restricted isometry property of centered self khatri rao products"
    - "binary component decomposition part ii the asymmetric case"
    - "improved scaling law for activity detection in massive mimo systems"
    - "sparse activity detection for massive connectivity"
    - "massive connectivity with massive mimo part i device activity detection and channel estimation"
    - "hierarchical compound poisson factorization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "convex relaxations of structured matrix factorizations"

Connected through hyperedge HE-3 (induced by source paper "bilinear parameterization for differentiable rank regularization"):
  with members:
    - "bilinear factor matrix norm minimization for robust pca algorithms and applications"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "nonconvex nonsmooth low rank minimization via iteratively reweighted nuclear norm"
    - "partial sum minimization of singular values in robust pca algorithm and applications"
    - "generalized nonconvex nonsmooth low rank minimization"
    - "convex relaxations of structured matrix factorizations"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-4 (induced by source paper "on the implicit bias of dropout"):
  with members:
    - "how to escape saddle points efficiently"
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "dropout training of matrix factorization and autoencoder for link prediction in sparse graphs"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the inductive bias of dropout"

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)