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: "self calibration and bilinear inverse problems via linear least squares"
  Abstract: Whenever we use devices to take measurements, calibration is indispensable. While the purpose of calibration is to reduce bias and uncertainty in the measurements, it can be quite difficult, expensive, and sometimes even impossible to implement. We study a challenging problem called \emph{self-calibration}, i.e., the task of designing an algorithm for devices so that the algorithm is able to perform calibration automatically. More precisely, we consider the setup $\boldsymbol{y} = \mathcal{A}(\boldsymbol{d}) \boldsymbol{x} + \boldsymbol{\epsilon}$ where only partial information about the sensing matrix $\mathcal{A}(\boldsymbol{d})$ is known and where $\mathcal{A}(\boldsymbol{d})$ linearly depends on $\boldsymbol{d}$. The goal is to estimate the calibration parameter $\boldsymbol{d}$ (resolve the uncertainty in the sensing process) and the signal/object of interests $\boldsymbol{x}$ simultaneously. For three different models of practical relevance, we show how such a \emph{bilinear} inverse problem, including blind deconvolution as an important example, can be solved via a simple \emph{linear least squares} approach. As a consequence, the proposed algorithms are numerically extremely efficient, thus potentially allowing for real-time deployment. We also present a variation of the least squares approach, which leads to a~\emph{spectral method}, where the solution to the bilinear inverse problem can be found by computing the singular vector associated with the smallest singular value of a certain matrix derived from the bilinear system. Explicit theoretical guarantees and stability theory are derived for both techniques; and the number of sampling complexity is nearly optimal (up to a poly-log factor). Applications in imaging sciences and signal processing are discussed and numerical simulations are presented to demonstrate the effectiveness and efficiency of our approach.

Connected through hyperedge HE-1 (induced by source paper "support recovery for sparse signals with unknown non stationary modulation"):
  with members:
    - "simultaneous sparse recovery and blind demodulation"
    - "sensor calibration for off the grid spectral estimation"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "self calibration and biconvex compressive sensing"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "blind deconvolution using convex programming"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-2 (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"
    - "spectral methods for passive imaging non asymptotic performance and robustness"
    - "sensor calibration for off the grid spectral estimation"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"

Connected through hyperedge HE-3 (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"
    - "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-4 (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"
    - "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-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"
    - "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"
    - "phasemax convex phase retrieval via basis pursuit"

Connected through hyperedge HE-6 (induced by source paper "from blind deconvolution to blind super resolution through convex programming"):
  with members:
    - "a non convex blind calibration method for randomised sensing strategies"
    - "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-7 (induced by source paper "spectral methods for passive imaging non asymptotic performance and robustness"):
  with members:
    - "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"
    - "fundamental limits of blind deconvolution part i ambiguity kernel"
    - "blind deconvolution using convex programming"

Connected through hyperedge HE-8 (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"
    - "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"

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)