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: "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
  Abstract: This paper explores robust recovery of a superposition of $R$ distinct complex exponential functions from a few random Gaussian projections. We assume that the signal of interest is of $2N-1$ dimensional and $R<<2N-1$. This framework covers a large class of signals arising from real applications in biology, automation, imaging science, etc. To reconstruct such a signal, our algorithm is to seek a low-rank Hankel matrix of the signal by minimizing its nuclear norm subject to the consistency on the sampled data. Our theoretical results show that a robust recovery is possible as long as the number of projections exceeds $O(R\ln^2N)$. No incoherence or separation condition is required in our proof. Our method can be applied to spectral compressed sensing where the signal of interest is a superposition of $R$ complex sinusoids. Compared to existing results, our result here does not need any separation condition on the frequencies, while achieving better or comparable bounds on the number of measurements. Furthermore, our method provides theoretical guidance on how many samples are required in the state-of-the-art non-uniform sampling in NMR spectroscopy. The performance of our algorithm is further demonstrated by numerical experiments.

Connected through hyperedge HE-1 (induced by source paper "atomic norm denoising for complex exponentials with unknown waveform modulations"):
  with members:
    - "approximate support recovery of atomic line spectral estimation a tale of resolution and precision"
    - "atomic norm minimization for modal analysis from random and compressed samples"
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "generalized line spectral estimation via convex optimization"
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "super resolution radar"

Connected through hyperedge HE-2 (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"
    - "self calibration and biconvex compressive sensing"
    - "blind deconvolution meets blind demixing algorithms and performance bounds"
    - "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-3 (induced by source paper "sparse methods for direction of arrival estimation"):
  with members:
    - "a compact formulation for the ell_ 2 1 mixed norm minimization problem"
    - "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"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "robust spectral compressed sensing via structured matrix completion"
    - "exact joint sparse frequency recovery via optimization methods"
    - "continuous compressed sensing with a single or multiple measurement vectors"

Connected through hyperedge HE-4 (induced by source paper "compressive parameter estimation via k median clustering"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "robust spectral compressed sensing via structured matrix completion"
    - "matched filtering from limited frequency samples"
    - "spectral compressive sensing with polar interpolation"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"

Connected through hyperedge HE-5 (induced by source paper "outlier detection using generative models with theoretical performance guarantees"):
  with members:
    - "cnn based projected gradient descent for consistent ct image reconstruction"
    - "separation free super resolution from compressed measurements is possible an orthonormal atomic norm minimization approach"
    - "admm net a deep learning approach for compressive sensing mri"
    - "adam a method for stochastic optimization"
    - "deep learning face attributes in the wild"
    - "recovery of sparsely corrupted signals"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-6 (induced by source paper "separation free super resolution from compressed measurements is possible an orthonormal atomic norm minimization approach"):
  with members:
    - "robust spectral compressed sensing via structured matrix completion"
    - "spectral super resolution with prior knowledge"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "towards a mathematical theory of super resolution"
    - "exact matrix completion via convex optimization"

Connected through hyperedge HE-7 (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"
    - "self calibration and biconvex compressive sensing"
    - "super resolution of positive sources the discrete setup"
    - "lifting for blind deconvolution in random mask imaging identifiability and convex relaxation"
    - "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-8 (induced by source paper "exact dimensionality reduction for partial line spectra estimation problems"):
  with members:
    - "super resolution of complex exponentials from modulations with unknown waveforms"
    - "vandermonde decomposition of multilevel toeplitz matrices with application to multidimensional super resolution"
    - "robust recovery of stream of pulses using convex optimization"
    - "off the grid line spectrum denoising and estimation with multiple measurement vectors"
    - "exact joint sparse frequency recovery via optimization methods"
    - "super resolution radar"
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"

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)