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: "convolutional dictionary learning through tensor factorization"
  Abstract: Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learning. Model parameters are estimated via CP decomposition of the observed higher order input moments. However, in many domains, additional invariances such as shift invariances exist, enforced via models such as convolutional dictionary learning. In this paper, we develop novel tensor decomposition algorithms for parameter estimation of convolutional models. Our algorithm is based on the popular alternating least squares method, but with efficient projections onto the space of stacked circulant matrices. Our method is embarrassingly parallel and consists of simple operations such as fast Fourier transforms and matrix multiplications. Our algorithm converges to the dictionary much faster and more accurately compared to the alternating minimization over filters and activation maps.

Connected through hyperedge HE-1 (induced by source paper "painless breakups efficient demixing of low rank matrices"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "sparse signal processing concepts for efficient 5g system design"
    - "low rank modeling and its applications in image analysis"
    - "convexity in source separation models geometry and algorithms"
    - "the achievable performance of convex demixing"
    - "blind deconvolution using convex programming"
    - "compressive principal component pursuit"

Connected through hyperedge HE-2 (induced by source paper "working locally thinking globally theoretical guarantees for convolutional sparse coding"):
  with members:
    - "on the global local dichotomy in sparsity modeling"
    - "boosting of image denoising algorithms"
    - "optimization methods for convolutional sparse coding"
    - "sparse modeling for image and vision processing"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"
    - "convolutional matching pursuit and dictionary training"

Connected through hyperedge HE-3 (induced by source paper "discovery of latent factors in high dimensional data using tensor methods"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "distributed representations of sentences and documents"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-4 (induced by source paper "unsupervised learning of word sequence representations from scratch via convolutional tensor decomposition"):
  with members:
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "natural language inference by tree based convolution and heuristic matching"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "distributed representations of sentences and documents"
    - "optimization methods for convolutional sparse coding"
    - "effective use of word order for text categorization with convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"):
  with members:
    - "on the global local dichotomy in sparsity modeling"
    - "working locally thinking globally theoretical guarantees for convolutional sparse coding"
    - "boosting of image denoising algorithms"
    - "optimization methods for convolutional sparse coding"

Connected through hyperedge HE-6 (induced by source paper "denoising and completion of 3d data via multidimensional dictionary learning"):
  with members:
    - "a tensor based dictionary learning approach to tomographic image reconstruction"
    - "novel methods for multilinear data completion and de noising based on tensor svd"

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)