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: "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"
  Abstract: We present a new algorithm for Independent Component Analysis (ICA) which has provable performance guarantees. In particular, suppose we are given samples of the form $y = Ax + \eta$ where $A$ is an unknown $n \times n$ matrix and $x$ is a random variable whose components are independent and have a fourth moment strictly less than that of a standard Gaussian random variable and $\eta$ is an $n$-dimensional Gaussian random variable with unknown covariance $\Sigma$: We give an algorithm that provable recovers $A$ and $\Sigma$ up to an additive $\epsilon$ and whose running time and sample complexity are polynomial in $n$ and $1 / \epsilon$. To accomplish this, we introduce a novel "quasi-whitening" step that may be useful in other contexts in which the covariance of Gaussian noise is not known in advance. We also give a general framework for finding all local optima of a function (given an oracle for approximately finding just one) and this is a crucial step in our algorithm, one that has been overlooked in previous attempts, and allows us to control the accumulation of error when we find the columns of $A$ one by one via local search.

Connected through hyperedge HE-1 (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-2 (induced by source paper "geometric methods for robust data analysis in high dimension"):
  with members:
    - "heavy tailed independent component analysis"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "fourier pca and robust tensor decomposition"
    - "a two round variant of em for gaussian mixtures"
    - "blind signal separation in the presence of gaussian noise"
    - "tensor decompositions for learning latent variable models"
    - "efficient learning of simplices"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"

Connected through hyperedge HE-3 (induced by source paper "polynomial time and sample complexity for non gaussian component analysis spectral methods"):
  with members:
    - "fourier pca and robust tensor decomposition"

Connected through hyperedge HE-4 (induced by source paper "robust estimators in high dimensions without the computational intractability"):
  with members:
    - "being robust in high dimensions can be practical"
    - "robust sparse estimation tasks in high dimensions"
    - "list decodable robust mean estimation and learning mixtures of spherical gaussians"
    - "mixture models robustness and sum of squares proofs"
    - "resilience a criterion for learning in the presence of arbitrary outliers"
    - "learning from untrusted data"
    - "statistical query lower bounds for robust estimation of high dimensional gaussians and gaussian mixtures"
    - "a size free clt for poisson multinomials and its applications"

Connected through hyperedge HE-5 (induced by source paper "symmetry saddle points and global geometry of nonconvex matrix factorization"):
  with members:
    - "no spurious local minima in nonconvex low rank problems a unified geometric analysis"
    - "the global optimization geometry of low rank matrix optimization"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"
    - "a geometric analysis of phase retrieval"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "provable efficient online matrix completion via non convex stochastic gradient descent"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-6 (induced by source paper "an improved analysis of the er spud dictionary learning algorithm"):
  with members:
    - "complete dictionary recovery over the sphere"
    - "more algorithms for provable dictionary learning"
    - "max vs min tensor decomposition and ica with nearly linear sample complexity"
    - "sparse modeling for image and vision processing"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "fourier pca and robust tensor decomposition"
    - "blind signal separation in the presence of gaussian noise"

Connected through hyperedge HE-7 (induced by source paper "escaping from saddle points online stochastic gradient for tensor decomposition"):
  with members:
    - "the loss surfaces of multilayer networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "exact solutions to the nonlinear dynamics of learning in deep linear neural networks"
    - "fast detection of overlapping communities via online tensor methods"
    - "tensor decompositions for learning latent variable models"
    - "making gradient descent optimal for strongly convex stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "a pseudo euclidean iteration for optimal recovery in noisy ica"):
  with members:
    - "fourier pca and robust tensor decomposition"

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)