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: "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
  Abstract: In this paper we analyze the blind deconvolution of an image and an unknown blur in a coded imaging system. The measurements consist of subsampled convolution of an unknown blurring kernel with multiple random binary modulations (coded masks) of the image. To perform the deconvolution, we consider a standard lifting of the image and the blurring kernel that transforms the measurements into a set of linear equations of the matrix formed by their outer product. Any rank-one solution to this system of equations provides a valid pair of an image and a blur. We first express the necessary and sufficient conditions for the uniqueness of a rank-one solution under some additional assumptions (uniform subsampling and no limit on the number of coded masks). These conditions are a special case of a previously established result regarding identifiability in the matrix completion problem. We also characterize a low-dimensional subspace model for the blur kernel that is sufficient to guarantee identifiability, includin...

Connected through hyperedge HE-1 (induced by source paper "blind deconvolution using modulated inputs"):
  with members:
    - "global geometry of multichannel sparse blind deconvolution on the sphere"
    - "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"

Connected through hyperedge HE-2 (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"
    - "a non convex blind calibration method for randomised sensing strategies"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "self calibration and bilinear inverse problems via linear least squares"

Connected through hyperedge HE-3 (induced by source paper "an overview of low rank matrix recovery from incomplete observations"):
  with members:
    - "consistent basis pursuit for signal and matrix estimates in quantized compressed sensing"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "poisson matrix recovery and completion"
    - "self calibration and biconvex compressive sensing"
    - "collaboratively learning preferences from ordinal data"
    - "categorical matrix completion"
    - "incoherence optimal matrix completion"

Connected through hyperedge HE-4 (induced by source paper "super resolution of complex exponentials from modulations with unknown waveforms"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "self calibration and biconvex compressive sensing"
    - "super resolution of positive sources the discrete setup"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"

Connected through hyperedge HE-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "empirical chaos processes and blind deconvolution"):
  with members:
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "recipes for stable linear embeddings from hilbert spaces to ℝ m"
    - "self calibration and biconvex compressive sensing"
    - "solving random quadratic systems of equations is nearly as easy as solving linear systems"
    - "rip like properties in subsampled blind deconvolution"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "blind recovery of sparse signals from subsampled convolution"

Connected through hyperedge HE-7 (induced by source paper "through the haze a non convex approach to blind gain calibration for linear random sensing models"):
  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"
    - "a geometric analysis of phase retrieval"
    - "a greedy blind calibration method for compressed sensing with unknown sensor gains"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "blind sensor calibration using approximate message passing"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"

Connected through hyperedge HE-8 (induced by source paper "a non convex blind calibration method for randomised sensing strategies"):
  with members:
    - "through the haze a non convex approach to blind gain calibration for linear random sensing models"
    - "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"
    - "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)