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: "a non convex blind calibration method for randomised sensing strategies"
  Abstract: The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that can be resource-consuming and cumbersome. Conversely, blind calibration does not require any training, 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 multiplicative factor (gain). To solve this optimisation problem we resort to projected gradient descent starting from a suitable initialisation. An analysis of this algorithm allows us to show that it converges to the global optimum provided a sample complexity requirement is met, i.e., relating convergence to the amount of information collected during the sensing process. Finally, we present some numerical experiments in which our algorithm allows for a simple solution to blind calibration of sensor gains in computational sensing applications.

Connected through hyperedge HE-1 (induced by source paper "sparse blind deconvolution for distributed radar autofocus imaging"):
  with members:
    - "blind gain and phase calibration via sparse spectral methods"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "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-2 (induced by source paper "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "non convex phase retrieval from stft measurements"
    - "fast algorithms for robust pca via gradient descent"
    - "phase retrieval using gauss newton method"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "blind gain and phase calibration via sparse spectral methods"):
  with members:
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "spectral methods for passive imaging non asymptotic performance and robustness"
    - "sensor calibration for off the grid spectral estimation"
    - "from blind deconvolution to blind super resolution through convex programming"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "phasemax convex phase retrieval via basis pursuit"

Connected through hyperedge HE-4 (induced by source paper "from blind deconvolution to blind super resolution through convex programming"):
  with members:
    - "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"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "compressive sampling of ensembles of correlated signals"

Connected through hyperedge HE-5 (induced by source paper "signal recovery in perturbed fourier compressed sensing"):
  with members:
    - "self calibration via linear least squares"
    - "self calibration and biconvex compressive sensing"
    - "compressive phase retrieval via generalized approximate message passing"
    - "convex optimization approaches for blind sensor calibration using sparsity"
    - "joint sparse recovery method for compressed sensing with structured dictionary mismatches"
    - "robustly stable signal recovery in compressed sensing with structured matrix perturbation"
    - "compressed sensing off the grid"
    - "joint sparsity with different measurement matrices"

Connected through hyperedge HE-6 (induced by source paper "a greedy blind calibration method for compressed sensing with unknown sensor gains"):
  with members:
    - "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"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "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:
    - "empirical chaos processes and blind deconvolution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "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 "rapid robust and reliable blind deconvolution via nonconvex optimization"):
  with members:
    - "fast algorithms for robust pca via gradient descent"
    - "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"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "sparse signal processing concepts for efficient 5g system design"
    - "phase retrieval via wirtinger flow theory and algorithms"

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)