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: "max vs min tensor decomposition and ica with nearly linear sample complexity"
  Abstract: We present a simple, general technique for reducing the sample complexity of matrix and tensor decomposition algorithms applied to distributions. We use the technique to give a polynomial-time algorithm for standard ICA with sample complexity nearly linear in the dimension, thereby improving substantially on previous bounds. The analysis is based on properties of random polynomials, namely the spacings of an ensemble of polynomials. Our technique also applies to other applications of tensor decompositions, including spherical Gaussian mixture models.

Connected through hyperedge HE-1 (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"
    - "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"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-2 (induced by source paper "agnostic estimation of mean and covariance"):
  with members:
    - "robust estimators in high dimensions without the computational intractability"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "fourier pca and robust tensor decomposition"
    - "blind signal separation in the presence of gaussian noise"
    - "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-3 (induced by source paper "tensor decompositions via two mode higher order svd hosvd"):
  with members:
    - "tensor factorization via matrix factorization"
    - "successive rank one approximations for nearly orthogonally decomposable symmetric tensors"
    - "a statistical model for tensor pca"
    - "uniqueness of tensor decompositions with applications to polynomial identifiability"
    - "tensor decompositions for learning latent variable models"
    - "a spectral algorithm for latent dirichlet allocation"
    - "most tensor problems are np hard"

Connected through hyperedge HE-4 (induced by source paper "super resolution off the grid"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "max vs min independent component analysis with nearly linear sample complexity"
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "a two round variant of em for gaussian mixtures"
    - "tensor decompositions for learning latent variable models"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"

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.DS(Data Structures and Algorithms)