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: "innovation rate sampling of pulse streams with application to ultrasound imaging"
  Abstract: Signals comprised of a stream of short pulses appear in many applications including bioimaging and radar. The recent finite rate of innovation framework, has paved the way to low rate sampling of such pulses by noticing that only a small number of parameters per unit time are needed to fully describe these signals. Unfortunately, for high rates of innovation, existing sampling schemes are numerically unstable. In this paper we propose a general sampling approach which leads to stable recovery even in the presence of many pulses. We begin by deriving a condition on the sampling kernel which allows perfect reconstruction of periodic streams from the minimal number of samples. We then design a compactly supported class of filters, satisfying this condition. The periodic solution is extended to finite and infinite streams and is shown to be numerically stable even for a large number of pulses. High noise robustness is also demonstrated when the delays are sufficiently separated. Finally, we process ultrasound imaging data using our techniques and show that substantial rate reduction with respect to traditional ultrasound sampling schemes can be achieved.

Connected through hyperedge HE-1 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain"):
  with members:
    - "a sampling framework for solving physics driven inverse source problems"
    - "on unlimited sampling"
    - "shift invariant and sampling spaces associated with the special affine fourier transform"
    - "stft phase retrieval uniqueness guarantees and recovery algorithms"
    - "frida fri based doa estimation for arbitrary array layouts"
    - "a theory of super resolution from short time fourier transform measurements"
    - "identification of linear time varying systems through waveform diversity"
    - "super resolution in phase space"

Connected through hyperedge HE-2 (induced by source paper "universal deep beamformer for variable rate ultrasound imaging"):
  with members:
    - "a deep learning framework for single sided sound speed inversion in medical ultrasound"
    - "sparse convolutional beamforming for ultrasound imaging"
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "high quality ultrasonic multi line transmission through deep learning"
    - "image reconstruction by domain transform manifold learning"
    - "high frame rate cardiac ultrasound imaging with deep learning"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"

Connected through hyperedge HE-3 (induced by source paper "sparsity based super resolution for sem images"):
  with members:
    - "image super resolution using deep convolutional networks"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "sparsity based sub wavelength imaging with partially incoherent light via quadratic compressed sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-4 (induced by source paper "component based modeling and processing of medical ultrasound signals"):
  with members:
    - "compressed beamforming applied to b mode ultrasound imaging"

Connected through hyperedge HE-5 (induced by source paper "stable support recovery of stream of pulses with application to ultrasound imaging"):
  with members:
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "robust recovery of positive stream of pulses"
    - "robust recovery of stream of pulses using convex optimization"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "exact recovery of dirac ensembles from the projection onto spaces of spherical harmonics"
    - "exact recovery of non uniform splines from the projection onto spaces of algebraic polynomials"

Connected through hyperedge HE-6 (induced by source paper "high resolution compressed sensing radar using difference set codes"):
  with members:
    - "sub nyquist radar via doppler focusing"
    - "structured compressed sensing from theory to applications"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "robust recovery of positive stream of pulses"):
  with members:
    - "compressive spectral estimation with single snapshot esprit stability and resolution"
    - "stable support recovery of stream of pulses with application to ultrasound imaging"
    - "music for multidimensional spectral estimation stability and super resolution"
    - "recovery of sparse positive signals on the sphere from low resolution measurements"
    - "super resolution of positive sources the discrete setup"
    - "support recovery for sparse deconvolution of positive measures"
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"

Connected through hyperedge HE-8 (induced by source paper "convex optimization approach for stable decomposition of stream of pulses"):
  with members:
    - "super resolution on the sphere using convex optimization"
    - "sparse spikes deconvolution on thin grids"
    - "compressed beamforming in ultrasound imaging"
    - "compressed sensing off the grid"
    - "sub nyquist radar via doppler focusing"

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)