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: "through the haze a non convex approach to blind gain calibration for linear random sensing models"
  Abstract: Computational sensing strategies often suffer from calibration errors in the physical implementation of their ideal sensing models. Such uncertainties are typically addressed by using multiple, accurately chosen training signals to recover the missing information on the sensing model, an approach that can be resource-consuming and cumbersome. Conversely, blind calibration does not employ any training signal, but corresponds to a bilinear inverse problem whose algorithmic solution is an open issue. We here address blind calibration as a non-convex problem for linear random sensing models, in which we aim to recover an unknown signal from its projections on sub-Gaussian random vectors, each subject to an unknown positive multiplicative factor (or gain). To solve this optimisation problem we resort to projected gradient descent starting from a suitable, carefully chosen initialisation point. An analysis of this algorithm allows us to show that it converges to the exact solution provided a sample complexity requirement is met, i.e., relating convergence to the amount of information collected during the sensing process. Interestingly, we show that this requirement grows linearly (up to log factors) in the number of unknowns of the problem. This sample complexity is found both in absence of prior information, as well as when subspace priors are available for both the signal and gains, allowing a further reduction of the number of observations required for our recovery guarantees to hold. Moreover, in the presence of noise we show how our descent algorithm yields a solution whose accuracy degrades gracefully with the amount of noise affecting the measurements. Finally, we present some numerical experiments in an imaging context, where our algorithm allows for a simple solution to blind calibration of the gains in a sensor array.

Connected through hyperedge HE-1 (induced by source paper "the basins of attraction of the global minimizers of the non convex sparse spikes estimation problem"):
  with members:
    - "support localization and the fisher metric for off the grid sparse regularization"
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "sketching for large scale learning of mixture models"
    - "compressive k means"
    - "adapting to unknown noise level in sparse deconvolution"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"

Connected through hyperedge HE-2 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging"):
  with members:
    - "blind gain and phase calibration via sparse spectral methods"
    - "a non convex blind calibration method for randomised sensing strategies"
    - "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"
    - "optimal sample complexity for blind gain and phase calibration"
    - "self calibration and biconvex compressive sensing"
    - "convex optimization approaches for blind sensor calibration using sparsity"

Connected through hyperedge HE-3 (induced by source paper "signal recovery from unlabeled samples"):
  with members:
    - "linear regression without correspondence"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "self calibration and biconvex compressive sensing"
    - "unlabeled sensing with random linear measurements"
    - "timing channels with multiple identical quanta"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "blind demixing and deconvolution at near optimal rate"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "guaranteed matrix completion via non convex factorization"
    - "a geometric analysis of phase retrieval"
    - "self calibration and biconvex compressive sensing"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "greed is super a fast algorithm for super resolution"

Connected through hyperedge HE-5 (induced by source paper "time for dithering fast and quantized random embeddings via the restricted isometry property"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "near optimal sample complexity bounds for circulant binary embedding"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "isometric sketching of any set via the restricted isometry property"
    - "representation and coding of signal geometry"
    - "small width low distortions quantized random embeddings of low complexity sets"

Connected through hyperedge HE-6 (induced by source paper "a greedy blind calibration method for compressed sensing with unknown sensor gains"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "a geometric analysis of phase retrieval"
    - "self calibration and biconvex compressive sensing"
    - "low complexity multiclass encryption by compressed sensing"
    - "blind sensor calibration using approximate message passing"

Connected through hyperedge HE-7 (induced by source paper "self calibration and bilinear inverse problems via linear least squares"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "leveraging diversity and sparsity in blind deconvolution"
    - "optimal sample complexity for blind gain and phase calibration"
    - "a greedy blind calibration method for compressed sensing with unknown sensor gains"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"

Connected through hyperedge HE-8 (induced by source paper "a non convex blind calibration method for randomised sensing strategies"):
  with members:
    - "a geometric analysis of phase retrieval"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "a parallel compressive imaging architecture for one shot acquisition"

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)