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: "subgradient descent learns orthogonal dictionaries"
  Abstract: This paper concerns dictionary learning, i.e., sparse coding, a fundamental representation learning problem. We show that a subgradient descent algorithm, with random initialization, can provably recover orthogonal dictionaries on a natural nonsmooth, nonconvex $\ell_1$ minimization formulation of the problem, under mild statistical assumptions on the data. This is in contrast to previous provable methods that require either expensive computation or delicate initialization schemes. Our analysis develops several tools for characterizing landscapes of nonsmooth functions, which might be of independent interest for provable training of deep networks with nonsmooth activations (e.g., ReLU), among numerous other applications. Preliminary experiments corroborate our analysis and show that our algorithm works well empirically in recovering orthogonal dictionaries.

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"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "hyperplane clustering via dual principal component pursuit"

Connected through hyperedge HE-2 (induced by source paper "complete dictionary learning via ell_p norm maximization"):
  with members:
    - "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "exact recovery of sparsely used dictionaries"
    - "robust principal component analysis"

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"
    - "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"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"

Connected through hyperedge HE-4 (induced by source paper "complete dictionary learning via 120001 4 norm maximization over the orthogonal group"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "a convergence theory for deep learning via over parameterization"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "fast and robust tensor decomposition with applications to dictionary learning"
    - "finding a sparse vector in a subspace linear sparsity using alternating directions"

Connected through hyperedge HE-5 (induced by source paper "multichannel sparse blind deconvolution on the sphere"):
  with members:
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "blind gain and phase calibration via sparse spectral methods"
    - "spectral methods for passive imaging non asymptotic performance and robustness"
    - "sensor calibration for off the grid spectral estimation"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-6 (induced by source paper "nonconvex robust low rank matrix recovery"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "the non convex geometry of low rank matrix optimization"
    - "global optimality in low rank matrix optimization"
    - "nonconvex low rank matrix recovery with arbitrary outliers via median truncated gradient descent"
    - "low rank positive semidefinite matrix recovery from corrupted rank one measurements"
    - "the global optimization geometry of low rank matrix optimization"
    - "fast algorithms for robust pca via gradient descent"
    - "matrix completion has no spurious local minimum"

Connected through hyperedge HE-7 (induced by source paper "dual principal component pursuit probability analysis and efficient algorithms"):
  with members:
    - "nonconvex robust low rank matrix recovery"
    - "fast robust and non convex subspace recovery"
    - "an overview of robust subspace recovery"
    - "thresholding based efficient outlier robust pca"
    - "a well tempered landscape for non convex robust subspace recovery"
    - "phase transitions of spectral initialization for high dimensional nonconvex estimation"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "provable self representation based outlier detection in a union of subspaces"

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)