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: "fundamental limits of blind deconvolution part i ambiguity kernel"
  Abstract: Blind deconvolution is an ubiquitous non-linear inverse problem in applications like wireless communications and image processing. This problem is generally ill-posed, and there have been efforts to use sparse models for regularizing blind deconvolution to promote signal identifiability. Part I of this two-part paper characterizes the ambiguity space of blind deconvolution and shows unidentifiability of this inverse problem for almost every pair of unconstrained input signals. The approach involves lifting the deconvolution problem to a rank one matrix recovery problem and analyzing the rank two null space of the resultant linear operator. A measure theoretically tight (parametric and recursive) representation of the key rank two null space is stated and proved. This representation is a novel foundational result for signal and code design strategies promoting identifiability under convolutive observation models. Part II of this paper analyzes the identifiability of sparsity constrained blind deconvolution and establishes surprisingly strong negative results on scaling laws for the sparsity-ambiguity trade-off.

Connected through hyperedge HE-1 (induced by source paper "blind phaseless short time fourier transform recovery"):
  with members:
    - "multi target detection with application to cryo electron microscopy"
    - "toward single particle reconstruction without particle picking breaking the detection limit"
    - "blind deconvolutional phase retrieval via convex programming"
    - "bispectrum inversion with application to multireference alignment"
    - "on signal reconstruction from frog measurements"
    - "stable blind deconvolution over the reals from additional autocorrelations"
    - "fourier phase retrieval uniqueness and algorithms"
    - "on the uniqueness of frog methods"

Connected through hyperedge HE-2 (induced by source paper "blind demixing and deconvolution at near optimal rate"):
  with members:
    - "regularized gradient descent a nonconvex recipe for fast joint blind deconvolution and demixing"
    - "recovering structured data from superimposed non linear measurements"
    - "robust nonnegative sparse recovery and the nullspace property of 0 1 measurements"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "low rank tensor recovery via iterative hard thresholding"
    - "optimal injectivity conditions for bilinear inverse problems with applications to identifiability of deconvolution problems"
    - "self calibration and biconvex compressive sensing"
    - "rip like properties in subsampled blind deconvolution"

Connected through hyperedge HE-3 (induced by source paper "stable blind deconvolution over the reals from additional autocorrelations"):
  with members:
    - "improved recovery guarantees for phase retrieval from coded diffraction patterns"
    - "blind demixing and deconvolution at near optimal rate"
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "explicit frames for deterministic phase retrieval via phaselift"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "sparse model uncertainties in compressed sensing with application to convolutions and sporadic communication"
    - "on the stability of sparse convolutions"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-4 (induced by source paper "spectral methods for passive imaging non asymptotic performance and robustness"):
  with members:
    - "self calibration and bilinear inverse problems via linear least squares"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "optimal sample complexity for blind gain and phase calibration"
    - "rip like properties in subsampled blind deconvolution"
    - "blind recovery of sparse signals from subsampled convolution"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-5 (induced by source paper "blind deconvolution with additional autocorrelations via convex programs"):
  with members:
    - "reconstruction of signals from their autocorrelation and cross correlation vectors with applications to phase retrieval and blind channel estimation"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "on the stability of sparse convolutions"
    - "stable recovery from the magnitude of symmetrized fourier measurements"
    - "compressed sensing on the image of bilinear maps"

Connected through hyperedge HE-6 (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-7 (induced by source paper "parametric bilinear generalized approximate message passing"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "identifiability in blind deconvolution under minimal assumptions"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs"

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)