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: "global geometry of multichannel sparse blind deconvolution on the sphere"
  Abstract: Multichannel blind deconvolution is the problem of recovering an unknown signal $f$ and multiple unknown channels $x_i$ from convolutional measurements $y_i=x_i \circledast f$ ($i=1,2,\dots,N$). We consider the case where the $x_i$'s are sparse, and convolution with $f$ is invertible. Our nonconvex optimization formulation solves for a filter $h$ on the unit sphere that produces sparse output $y_i\circledast h$. Under some technical assumptions, we show that all local minima of the objective function correspond to the inverse filter of $f$ up to an inherent sign and shift ambiguity, and all saddle points have strictly negative curvatures. This geometric structure allows successful recovery of $f$ and $x_i$ using a simple manifold gradient descent algorithm with random initialization. Our theoretical findings are complemented by numerical experiments, which demonstrate superior performance of the proposed approach over the previous methods.

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"
    - "an overview of robust subspace recovery"
    - "subgradient descent learns orthogonal dictionaries"
    - "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"
    - "subgradient descent learns orthogonal dictionaries"
    - "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"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "subgradient descent learns orthogonal dictionaries"
    - "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"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "subgradient descent learns orthogonal dictionaries"
    - "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 "subgradient descent learns orthogonal dictionaries"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "rate optimal denoising with deep neural networks"
    - "nonconvex robust low rank matrix recovery"
    - "learning relu networks via alternating minimization"
    - "stochastic gradient descent learns state equations with nonlinear activations"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "deep denoising rate optimal recovery of structured signals with a deep prior"
    - "improved learning of one hidden layer convolutional neural networks with overlaps"

Connected through hyperedge HE-6 (induced by source paper "multichannel sparse blind deconvolution on the sphere"):
  with members:
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "subgradient descent learns orthogonal dictionaries"
    - "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-7 (induced by source paper "blind deconvolution using modulated inputs"):
  with members:
    - "blind gain and phase calibration via sparse spectral methods"
    - "spectral methods for passive imaging non asymptotic performance and robustness"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "leveraging diversity and sparsity in blind deconvolution"
    - "guaranteed matrix completion via non convex factorization"
    - "optimal sample complexity for blind gain and phase calibration"

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)