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: "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
  Abstract: Abstract   This article gives theoretical insights into the performance of K-SVD, a dictionary learning algorithm that has gained significant popularity in practical applications. The particular question studied here is when a dictionary    Φ  ∈    R    d  ×  K      can be recovered as local minimum of the minimisation criterion underlying K-SVD from a set of N training signals      y    n    =  Φ    x    n     . A theoretical analysis of the problem leads to two types of identifiability results assuming the training signals are generated from a tight frame with coefficients drawn from a random symmetric distribution. First, asymptotic results showing that in expectation the generating dictionary can be recovered exactly as a local minimum of the K-SVD criterion if the coefficient distribution exhibits sufficient decay. Second, based on the asymptotic results it is demonstrated that given a finite number of training samples N, such that    N  /  log  ⁡  N  =  O  (    K    3    d  )   , except with probability    O  (    N    −  K  d    )    there is a local minimum of the K-SVD criterion within distance    O  (  K    N    −  1  /  4    )    to the generating dictionary.

Connected through hyperedge HE-1 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms"):
  with members:
    - "minimax lower bounds on dictionary learning for tensor data"
    - "tensorizing neural networks"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "when are overcomplete topic models identifiable uniqueness of tensor tucker decompositions with structured sparsity"

Connected through hyperedge HE-2 (induced by source paper "minimax lower bounds on dictionary learning for tensor data"):
  with members:
    - "minimax lower bounds for kronecker structured dictionary learning"
    - "a tensor based dictionary learning approach to tomographic image reconstruction"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"

Connected through hyperedge HE-3 (induced by source paper "complete dictionary recovery over the sphere i overview and the geometric picture"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "polynomial time tensor decompositions with sum of squares"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "simple efficient and neural algorithms for sparse coding"

Connected through hyperedge HE-4 (induced by source paper "dictionary learning from incomplete data"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "complete dictionary recovery over the sphere"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-5 (induced by source paper "minimax lower bounds for kronecker structured dictionary learning"):
  with members:
    - "minimax lower bounds on dictionary learning for tensor data"
    - "a tensor based dictionary learning approach to tomographic image reconstruction"
    - "denoising and completion of 3d data via multidimensional dictionary learning"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

Connected through hyperedge HE-6 (induced by source paper "complete dictionary recovery over the sphere"):
  with members:
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"
    - "guaranteed matrix completion via non convex factorization"
    - "simple efficient and neural algorithms for sparse coding"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "on the computational efficiency of training neural networks"
    - "more algorithms for provable dictionary learning"

Connected through hyperedge HE-7 (induced by source paper "convergence radius and sample complexity of itkm algorithms for dictionary learning"):
  with members:
    - "simple efficient and neural algorithms for sparse coding"
    - "complete dictionary recovery over the sphere"
    - "more algorithms for provable dictionary learning"
    - "local identification of overcomplete dictionaries"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "dictionary learning and tensor decomposition via the sum of squares method"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "ripless compressed sensing from anisotropic measurements"

Connected through hyperedge HE-8 (induced by source paper "simple efficient and neural algorithms for sparse coding"):
  with members:
    - "local identification of overcomplete dictionaries"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "understanding alternating minimization for matrix completion"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"
    - "exact recovery of sparsely used 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.IT(Information Theory)