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: "blind signal separation in the presence of gaussian noise"
  Abstract: A prototypical blind signal separation problem is the so-called cocktail party problem, with n people talking simultaneously and n different microphones within a room. The goal is to recover each speech signal from the microphone inputs. Mathematically this can be modeled by assuming that we are given samples from an n-dimensional random variable X=AS, where S is a vector whose coordinates are independent random variables corresponding to each speaker. The objective is to recover the matrix A^{-1} given random samples from X. A range of techniques collectively known as Independent Component Analysis (ICA) have been proposed to address this problem in the signal processing and machine learning literature. Many of these techniques are based on using the kurtosis or other cumulants to recover the components. #R##N#In this paper we propose a new algorithm for solving the blind signal separation problem in the presence of additive Gaussian noise, when we are given samples from X=AS+\eta, where \eta is drawn from an unknown, not necessarily spherical n-dimensional Gaussian distribution. Our approach is based on a method for decorrelating a sample with additive Gaussian noise under the assumption that the underlying distribution is a linear transformation of a distribution with independent components. Our decorrelation routine is based on the properties of cumulant tensors and can be combined with any standard cumulant-based method for ICA to get an algorithm that is provably robust in the presence of Gaussian noise. We derive polynomial bounds for the sample complexity and error propagation of our method.

Connected through hyperedge HE-1 (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"
    - "tensor decompositions for learning latent variable models"
    - "efficient learning of simplices"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"

Connected through hyperedge HE-2 (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"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-3 (induced by source paper "agnostic estimation of mean and covariance"):
  with members:
    - "robust estimators in high dimensions without the computational intractability"
    - "max vs min tensor decomposition and ica with nearly linear sample complexity"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "fourier pca and robust tensor decomposition"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"
    - "robust principal component analysis"

Connected through hyperedge HE-4 (induced by source paper "eigenvectors of orthogonally decomposable functions"):
  with members:
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "the hidden convexity of spectral clustering"
    - "fourier pca and robust tensor decomposition"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"

Connected through hyperedge HE-5 (induced by source paper "max vs min tensor decomposition and ica with nearly linear sample complexity"):
  with members:
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "fourier pca and robust tensor decomposition"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a spectral algorithm for latent dirichlet allocation"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"
    - "most tensor problems are np hard"

Connected through hyperedge HE-6 (induced by source paper "fourier pca and robust tensor decomposition"):
  with members:
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "smoothed analysis of tensor decompositions"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "tensor decompositions for learning latent variable models"
    - "efficient learning of simplices"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a method of moments for mixture models and hidden markov models"

Connected through hyperedge HE-7 (induced by source paper "smoothed analysis of tensor decompositions"):
  with members:
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "fourier pca and robust tensor decomposition"
    - "tensor decompositions for learning latent variable models"
    - "learning mixtures of arbitrary distributions over large discrete domains"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "a method of moments for mixture models and hidden markov models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"

Connected through hyperedge HE-8 (induced by source paper "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"):
  with members:
    - "smoothed analysis of tensor decompositions"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "a two round variant of em for gaussian mixtures"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"
    - "settling the polynomial learnability of mixtures of gaussians"
    - "polynomial learning of distribution families"

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)