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: "learning sparsely used overcomplete dictionaries via alternating minimization"
  Abstract: We consider the problem of sparse coding, where each sample consists of a sparse linear combination of a set of dictionary atoms, and the task is to learn both the dictionary elements and the mixing coefficients. Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficients are estimated in alternate steps, keeping the other fixed. Typically, the coefficients are estimated via $\ell_1$ minimization, keeping the dictionary fixed, and the dictionary is estimated through least squares, keeping the coefficients fixed. In this paper, we establish local linear convergence for this variant of alternating minimization and establish that the basin of attraction for the global optimum (corresponding to the true dictionary and the coefficients) is $\order{1/s^2}$, where $s$ is the sparsity level in each sample and the dictionary satisfies RIP. Combined with the recent results of approximate dictionary estimation, this yields provable guarantees for exact recovery of both the dictionary elements and the coefficients, when the dictionary elements are incoherent.

Connected through hyperedge HE-1 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "nonconvex robust low rank matrix recovery"
    - "dual principal component pursuit probability analysis and efficient algorithms"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"

Connected through hyperedge HE-2 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions"):
  with members:
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "handling incomplete heterogeneous data using vaes"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "simple efficient and neural algorithms for sparse coding"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "deep networks for image super resolution with sparse prior"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "analysis of the optimization landscapes for overcomplete representation learning"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "subgradient descent learns orthogonal dictionaries"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "on the optimization landscape of tensor decompositions"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-4 (induced by source paper "deep residual auto encoders for expectation maximization based dictionary learning"):
  with members:
    - "on the convergence of adam and beyond"
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "cyclical learning rates for training neural networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "deep unfolding model based inspiration of novel deep architectures"
    - "k sparse autoencoders"

Connected through hyperedge HE-5 (induced by source paper "randnet deep learning with compressed measurements of images"):
  with members:
    - "deep residual auto encoders for expectation maximization based dictionary learning"
    - "scalable convolutional dictionary learning with constrained recurrent sparse auto encoders"
    - "discriminative recurrent sparse auto encoders"

Connected through hyperedge HE-6 (induced by source paper "towards learning sparsely used dictionaries with arbitrary supports"):
  with members:
    - "outlier robust moment estimation via sum of squares"
    - "clustering semi random mixtures of gaussians"
    - "an improved analysis of the er spud dictionary learning algorithm"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "on the effect of randomness on planted 3 coloring models"
    - "simple efficient and neural algorithms for sparse coding"
    - "a semidefinite program for unbalanced multisection in the stochastic block model"

Connected through hyperedge HE-7 (induced by source paper "analysis of fast structured dictionary learning"):
  with members:
    - "learning multi layer transform models"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "a fast patch dictionary method for whole image recovery"
    - "new algorithms for learning incoherent and overcomplete dictionaries"

Connected through hyperedge HE-8 (induced by source paper "analysis of fast alternating minimization for structured dictionary learning"):
  with members:
    - "analysis of fast structured dictionary learning"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "a fast patch dictionary method for whole image recovery"
    - "new algorithms for learning incoherent and overcomplete dictionaries"

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)