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: "spectral methods for passive imaging non asymptotic performance and robustness"
  Abstract: We study the problem of passive imaging through convolutive channels. A scene is illuminated with an unknown, unstructured source, and the measured response is the convolution of this source with multiple channel responses, each of which is time-limited. Spectral methods based on the commutativity of convolution, first proposed and analyzed in the 1990s, provide an elegant mathematical framework for attacking this problem. However, these now classical methods are very sensitive to noise, especially when working from relatively small sample sizes. #R##N#In this paper, we show that a linear subspace model on the coefficients of the impulse responses of the channels can make this problem well-posed. We derive non-asymptotic error bounds for the generic subspace model by analyzing the spectral gap of the cross-correlation matrix of the channels relative to the perturbation introduced by noise. Numerical results show that this modified spectral method offers significant improvements over the classical method and outperforms other competing methods for multichannel blind deconvolution.

Connected through hyperedge HE-1 (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"
    - "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"
    - "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-2 (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"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "sparse power factorization balancing peakiness and sample complexity"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "solving equations of random convex functions via anchored regression"
    - "blind demixing and deconvolution at near optimal rate"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"
    - "improved bounds for sparse recovery from subsampled random convolutions"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"

Connected through hyperedge HE-4 (induced by source paper "global geometry of multichannel sparse blind deconvolution on the sphere"):
  with members:
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "blind gain and phase calibration via sparse spectral methods"
    - "sensor calibration for off the grid spectral estimation"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-5 (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"
    - "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"
    - "phasemax convex phase retrieval via basis pursuit"

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)