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: "blind deconvolution meets blind demixing algorithms and performance bounds"
  Abstract: Suppose that we have $r$ sensors and each one intends to send a function $\boldsymbol{g}_i$ (e.g.\ a signal or an image) to a receiver common to all $r$ sensors. During transmission, each $\boldsymbol{g}_i$ gets convolved with a function $\boldsymbol{f}_i$. The receiver records the function $\boldsymbol{y}$, given by the sum of all these convolved signals. When and under which conditions is it possible to recover the individual signals $\boldsymbol{g}_i$ and the blurring functions $\boldsymbol{f}_i$ from just one received signal $\boldsymbol{y}$? This challenging problem, which intertwines blind deconvolution with blind demixing, appears in a variety of applications, such as audio processing, image processing, neuroscience, spectroscopy, and astronomy. It is also expected to play a central role in connection with the future Internet-of-Things. We will prove that under reasonable and practical assumptions, it is possible to solve this otherwise highly ill-posed problem and recover the $r$ transmitted functions $\boldsymbol{g}_i$ and the impulse responses $\boldsymbol{f}_i$ in a robust, reliable, and efficient manner from just one single received function $\boldsymbol{y}$ by solving a semidefinite program. We derive explicit bounds on the number of measurements needed for successful recovery and prove that our method is robust in the presence of noise. Our theory is actually sub-optimal, since numerical experiments demonstrate that, quite remarkably, recovery is still possible if the number of measurements is close to the number of degrees of freedom.

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"
    - "self calibration and bilinear inverse problems via linear least squares"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "self calibration and biconvex compressive sensing"
    - "blind deconvolution using convex programming"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "on the convex geometry of blind deconvolution and matrix completion"):
  with members:
    - "phase retrieval without small ball probability assumptions"
    - "blind deconvolution by a steepest descent algorithm on a quotient manifold"
    - "blind demixing and deconvolution at near optimal rate"
    - "implicit regularization in nonconvex statistical estimation gradient descent converges linearly for phase retrieval matrix completion and blind deconvolution"
    - "low rank matrix recovery from clifford orbits"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "matrix completion has no spurious local minimum"
    - "guaranteed matrix completion via non convex factorization"

Connected through hyperedge HE-3 (induced by source paper "interference removal for radar communication co existence the random scattering case"):
  with members:
    - "on the global geometry of sphere constrained sparse blind deconvolution"
    - "adaptive interference removal for uncoordinated radar communication coexistence"
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "recovery of structured signals with prior information via maximizing correlation"
    - "joint design of overlaid communication systems and pulsed radars"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "spectrum sharing between a surveillance radar and secondary wi fi networks"
    - "optimum co design for spectrum sharing between matrix completion based mimo radars and a mimo communication system"

Connected through hyperedge HE-4 (induced by source paper "exact matrix completion based on low rank hankel structure in the fourier domain"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "blind demixing and deconvolution at near optimal rate"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "incoherence optimal matrix completion"
    - "exact tensor completion using t svd"
    - "robust spectral compressed sensing via structured matrix completion"
    - "the achievable performance of convex demixing"

Connected through hyperedge HE-5 (induced by source paper "multi target detection with application to cryo electron microscopy"):
  with members:
    - "multi target detection with an arbitrary spacing distribution"
    - "toward single particle reconstruction without particle picking breaking the detection limit"
    - "bispectrum inversion with application to multireference alignment"
    - "heterogeneous multireference alignment a single pass approach"
    - "fourier phase retrieval uniqueness and algorithms"
    - "the sample complexity of multi reference alignment"
    - "multireference alignment is easier with an aperiodic translation distribution"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"

Connected through hyperedge HE-6 (induced by source paper "simultaneous sparse recovery and blind demodulation"):
  with members:
    - "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"
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "self calibration and biconvex compressive sensing"
    - "blind deconvolution using convex programming"
    - "recovering low rank matrices from few coefficients in any basis"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "support recovery for sparse signals with 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 using convex programming"
    - "a probabilistic and ripless theory of compressed sensing"

Connected through hyperedge HE-8 (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"

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)