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: "convergence radius and sample complexity of itkm algorithms for dictionary learning"
  Abstract: In this work we show that iterative thresholding and K-means (ITKM) algorithms can recover a generating dictionary with K atoms from noisy $S$ sparse signals up to an error $\tilde \varepsilon$ as long as the initialisation is within a convergence radius, that is up to a $\log K$ factor inversely proportional to the dynamic range of the signals, and the sample size is proportional to $K \log K \tilde \varepsilon^{-2}$. The results are valid for arbitrary target errors if the sparsity level is of the order of the square root of the signal dimension $d$ and for target errors down to $K^{-\ell}$ if $S$ scales as $S \leq d/(\ell \log K)$.

Connected through hyperedge HE-1 (induced by source paper "generalization error in deep learning"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "adversarially robust generalization requires more data"
    - "deep neural networks with multi branch architectures are less non convex"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-2 (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"
    - "a fast patch dictionary method for whole image recovery"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "learning sparsely used overcomplete dictionaries via alternating minimization"

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"
    - "complete dictionary recovery over the sphere"
    - "on the identifiability of overcomplete dictionaries via the minimisation principle underlying k svd"
    - "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 "sequential learning of analysis operators"):
  with members:
    - "learning co sparse analysis operators with separable structures"
    - "local identification of overcomplete dictionaries"
    - "analysis operator learning and its application to image reconstruction"

Connected through hyperedge HE-6 (induced by source paper "online and stable learning of analysis operators"):
  with members:
    - "local identification of overcomplete dictionaries"
    - "analysis operator learning and its application to image reconstruction"

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"
    - "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"
    - "more algorithms for provable dictionary learning"

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)