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: "convex relaxations of structured matrix factorizations"
  Abstract: We consider the factorization of a rectangular matrix $X $ into a positive linear combination of rank-one factors of the form $u v^\top$, where $u$ and $v$ belongs to certain sets $\mathcal{U}$ and $\mathcal{V}$, that may encode specific structures regarding the factors, such as positivity or sparsity. In this paper, we show that computing the optimal decomposition is equivalent to computing a certain gauge function of $X$ and we provide a detailed analysis of these gauge functions and their polars. Since these gauge functions are typically hard to compute, we present semi-definite relaxations and several algorithms that may recover approximate decompositions with approximation guarantees. We illustrate our results with simulations on finding decompositions with elements in $\{0,1\}$. As side contributions, we present a detailed analysis of variational quadratic representations of norms as well as a new iterative basis pursuit algorithm that can deal with inexact first-order oracles.

Connected through hyperedge HE-1 (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"
    - "structured low rank matrix factorization global optimality algorithms and applications"
    - "hierarchical compound poisson factorization"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-2 (induced by source paper "binary component decomposition part ii the asymmetric case"):
  with members:
    - "binary component decomposition part i the positive semidefinite case"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "robust computation of linear models by convex relaxation"
    - "learning without concentration"
    - "robust principal component analysis"

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"
    - "structured low rank matrix factorization global optimality 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"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-4 (induced by source paper "faster rates for convex concave games"):
  with members:
    - "introduction to online convex optimization"
    - "fast convergence of regularized learning in games"

Connected through hyperedge HE-5 (induced by source paper "an analysis of dropout for matrix factorization"):
  with members:
    - "dropout training of matrix factorization and autoencoder for link prediction in sparse graphs"
    - "global optimality in tensor factorization deep learning and beyond"
    - "on the inductive bias of dropout"
    - "adding noise to the input of a model trained with a regularized objective"
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-6 (induced by source paper "structured low rank matrix factorization global optimality algorithms and applications"):
  with members:
    - "convex sparse matrix factorizations"

Connected through hyperedge HE-7 (induced by source paper "pursuits in structured non convex matrix factorizations"):
  with members:
    - "the power of convex relaxation near optimal matrix completion"
    - "robust principal component analysis"
    - "convex sparse matrix factorizations"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-8 (induced by source paper "forward backward greedy algorithms for atomic norm regularization"):
  with members:
    - "convexity in source separation models geometry and algorithms"
    - "sparse overlapping sets lasso for multitask learning and its application to fmri analysis"
    - "solving oscar regularization problems by proximal splitting algorithms"
    - "tensor decompositions for learning latent variable models"
    - "block coordinate frank wolfe optimization for structural svms"
    - "high dimensional sparse inverse covariance estimation using greedy methods"
    - "orthogonal matching pursuit with replacement"
    - "convex approaches to model wavelet sparsity patterns"

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)