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: "structure from local optima learning subspace juntas via higher order pca"
  Abstract: We present a generalization of the well-known problem of learning k-juntas in R^n, and a novel tensor algorithm for unraveling the structure of high-dimensional distributions. Our algorithm can be viewed as a higher-order extension of Principal Component Analysis (PCA). #R##N#Our motivating problem is learning a labeling function in R^n, which is determined by an unknown k-dimensional subspace. This problem of learning a k-subspace junta is a common generalization of learning a k-junta (a function of k coordinates in R^n) and learning intersections of k halfspaces. In this context, we introduce an irrelevant noisy attributes model where the distribution over the "relevant" k-dimensional subspace is independent of the distribution over the (n-k)-dimensional "irrelevant" subspace orthogonal to it. #R##N#We give a spectral tensor algorithm which identifies the relevant subspace, and thereby learns k-subspace juntas under some additional assumptions. We do this by exploiting the structure of local optima of higher moment tensors over the unit sphere; PCA finds the global optima of the second moment tensor (covariance matrix). Our main result is that when the distribution in the irrelevant (n-k)-dimensional subspace is any Gaussian, the complexity of our algorithm is T(k,\epsilon) + \poly(n), where T is the complexity of learning the concept in k dimensions, and the polynomial is a function of the k-dimensional concept class being learned. This substantially generalizes existing results on learning low-dimensional concepts.

Connected through hyperedge HE-1 (induced by source paper "non gaussian component analysis using entropy methods"):
  with members:
    - "heavy tailed independent component analysis"

Connected through hyperedge HE-2 (induced by source paper "efficient approaches for escaping higher order saddle points in non convex optimization"):
  with members:
    - "on the quality of the initial basin in overspecified neural networks"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"

Connected through hyperedge HE-3 (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"
    - "blind signal separation in the presence of gaussian noise"
    - "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"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "blind signal separation in the presence of gaussian noise"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "efficient learning of simplices"):
  with members:
    - "tensor decompositions for learning latent variable models"
    - "a method of moments for mixture models and hidden markov models"
    - "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"
    - "learning convex bodies is hard"

Connected through hyperedge HE-7 (induced by source paper "provable ica with unknown gaussian noise and implications for gaussian mixtures and autoencoders"):
  with members:
    - "smoothed analysis of tensor decompositions"
    - "the more the merrier the blessing of dimensionality for learning large gaussian mixtures"
    - "blind signal separation in the presence of gaussian noise"
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"

Connected through hyperedge HE-8 (induced by source paper "blind signal separation in the presence of gaussian noise"):
  with members:
    - "learning mixtures of spherical gaussians moment methods and spectral decompositions"

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.CC(Computational Complexity)