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: "local identification of overcomplete dictionaries"
  Abstract: This paper presents the first theoretical results showing that stable identification of overcomplete $\mu$-coherent dictionaries $\Phi \in \mathbb{R}^{d\times K}$ is locally possible from training signals with sparsity levels $S$ up to the order $O(\mu^{-2})$ and signal to noise ratios up to $O(\sqrt{d})$. In particular the dictionary is recoverable as the local maximum of a new maximisation criterion that generalises the K-means criterion. For this maximisation criterion results for asymptotic exact recovery for sparsity levels up to $O(\mu^{-1})$ and stable recovery for sparsity levels up to $O(\mu^{-2})$ as well as signal to noise ratios up to $O(\sqrt{d})$ are provided. These asymptotic results translate to finite sample size recovery results with high probability as long as the sample size $N$ scales as $O(K^3dS \tilde \varepsilon^{-2})$, where the recovery precision $\tilde \varepsilon$ can go down to the asymptotically achievable precision. Further, to actually find the local maxima of the new criterion, a very simple Iterative Thresholding and K (signed) Means algorithm (ITKM), which has complexity $O(dKN)$ in each iteration, is presented and its local efficiency is demonstrated in several experiments.

Connected through hyperedge HE-1 (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"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "sparse and spurious dictionary learning with noise and outliers"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "separable dictionary learning"

Connected through hyperedge HE-2 (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-3 (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"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "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-4 (induced by source paper "sequential learning of analysis operators"):
  with members:
    - "learning co sparse analysis operators with separable structures"
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-5 (induced by source paper "online and stable learning of analysis operators"):
  with members:
    - "convergence radius and sample complexity of itkm algorithms for dictionary learning"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-6 (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"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "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-7 (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 identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "on the computational efficiency of training neural networks"

Connected through hyperedge HE-8 (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"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "more algorithms for provable dictionary learning"
    - "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"

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)