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: "the phase transition of matrix recovery from gaussian measurements matches the minimax mse of matrix denoising"
  Abstract: Let be an unknown M by N matrix. In matrix recovery, one takes linear measurements of , where and each is an M by N matrix. A popular approach for matrix recovery is nuclear norm minimization (NNM): solving the convex optimization problem for all , where denotes the nuclear norm, namely, the sum of singular values. Empirical work reveals a phase transition curve, stated in terms of the undersampling fraction , rank fraction , and aspect ratio . Specifically when the measurement matrices Ai have independent standard Gaussian random entries, a curve exists such that, if , NNM typically succeeds for large M,N, whereas if , it typically fails. An apparently quite different problem is matrix denoising in Gaussian noise, in which an unknown M by N matrix is to be estimated based on direct noisy measurements , where the matrix Z has independent and identically distributed Gaussian entries. A popular matrix denoising scheme solves the unconstrained optimization problem . When optimally tuned, this scheme achieves the asymptotic minimax mean-squared error , where . We report extensive experiments showing that the phase transition in the first problem, matrix recovery from Gaussian measurements, coincides with the minimax risk curve in the second problem, matrix denoising in Gaussian noise: , for any rank fraction (at each common aspect ratio β). Our experiments considered matrices belonging to two constraint classes: real M by N matrices, of various ranks and aspect ratios, and real symmetric positive-semidefinite N by N matrices, of various ranks.

Connected through hyperedge HE-1 (induced by source paper "implicit regularization of normalization methods"):
  with members:
    - "over parameterization as a catalyst for better generalization of deep relu network"
    - "traditional and heavy tailed self regularization in neural network models"
    - "two models of double descent for weak features"
    - "a quantitative analysis of the effect of batch normalization on gradient descent"
    - "implicit regularization in deep matrix factorization"
    - "theoretical issues in deep networks approximation optimization and generalization"
    - "luck matters understanding training dynamics of deep relu networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"

Connected through hyperedge HE-2 (induced by source paper "near optimal matrix recovery from random linear measurements"):
  with members:
    - "phase diagram of matrix compressed sensing"
    - "parametric bilinear generalized approximate message passing"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "sharp time data tradeoffs for linear inverse problems"
    - "bilinear generalized approximate message passing part i derivation"
    - "low rank modeling and its applications in image analysis"
    - "from denoising to compressed sensing"
    - "living on the edge phase transitions in convex programs with random data"

Connected through hyperedge HE-3 (induced by source paper "phase transition of convex programs for linear inverse problems with multiple prior constraints"):
  with members:
    - "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"

Connected through hyperedge HE-4 (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-5 (induced by source paper "state evolution for approximate message passing with non separable functions"):
  with members:
    - "near optimal matrix recovery from random linear measurements"
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "hybrid approximate message passing"
    - "dynamical functional theory for compressed sensing"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "approximate message passing with restricted boltzmann machine priors"
    - "compressive phase retrieval via generalized approximate message passing"

Connected through hyperedge HE-6 (induced by source paper "complete dictionary recovery over the sphere"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "guaranteed matrix completion via non convex factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "on the computational efficiency of training neural networks"

Connected through hyperedge HE-7 (induced by source paper "sharp mse bounds for proximal denoising"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "a performance analysis framework for socp algorithms in noisy compressed sensing"
    - "a framework to characterize performance of lasso algorithms"
    - "the squared error of generalized lasso a precise analysis"
    - "guarantees of total variation minimization for signal recovery"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "atomic norm denoising with applications to line spectral estimation"

Connected through hyperedge HE-8 (induced by source paper "the squared error of generalized lasso a precise analysis"):
  with members:
    - "corrupted sensing novel guarantees for separating structured signals"
    - "the achievable performance of convex demixing"
    - "living on the edge phase transitions in convex programs with random data"
    - "regularly random duality"
    - "a performance analysis framework for socp algorithms in noisy compressed sensing"
    - "a framework to characterize performance of lasso algorithms"
    - "sharp mse bounds for proximal denoising"
    - "a rigorous geometry probability equivalence in characterization of ell_1 optimization"

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.IT(Information Theory)