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: "fourier domain beamforming the path to compressed ultrasound imaging"
  Abstract: Sonography techniques use multiple transducer elements for tissue visualization. Signals detected at each element are sampled prior to digital beamforming. The sampling rates required to perform high resolution digital beamforming are significantly higher than the Nyquist rate of the signal and result in considerable amount of data, that needs to be stored and processed. A recently developed technique, compressed beamforming, based on the finite rate of innovation model, compressed sensing (CS) and Xampling ideas, allows to reduce the number of samples needed to reconstruct an image comprised of strong reflectors. A drawback of this method is its inability to treat speckle, which is of significant importance in medical imaging. Here we build on previous work and extend it to a general concept of beamforming in frequency. This allows to exploit the low bandwidth of the ultrasound signal and bypass the oversampling dictated by digital implementation of beamforming in time. Using beamforming in frequency, the same image quality is obtained from far fewer samples. We next present a CS-technique that allows for further rate reduction, using only a portion of the beamformed signal's bandwidth. We demonstrate our methods on in vivo cardiac data and show that reductions up to 1/28 over standard beamforming rates are possible. Finally, we present an implementation on an ultrasound machine using sub-Nyquist sampling and processing. Our results prove that the concept of sub-Nyquist processing is feasible for medical ultrasound, leading to the potential of considerable reduction in future ultrasound machines size, power consumption and cost.

Connected through hyperedge HE-1 (induced by source paper "sparse convolutional beamforming for ultrasound imaging"):
  with members:
    - "compressed beamforming in ultrasound imaging"

Connected through hyperedge HE-2 (induced by source paper "sub nyquist sar via fourier domain range doppler processing"):
  with members:
    - "fast compressed sensing sar imaging based on approximated observation"
    - "recovery of sparse matrices via matrix sketching"
    - "xampling at the rate of innovation"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "sub nyquist sampling bridging theory and practice"

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"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "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 "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-5 (induced by source paper "summer sub nyquist mimo radar"):
  with members:
    - "cognitive sub nyquist hardware prototype of a collocated mimo radar"
    - "mimo mc radar a mimo radar approach based on matrix completion"
    - "spatial compressive sensing for mimo radar"
    - "recovery of sparse matrices via matrix sketching"
    - "matrix completion in colocated mimo radar recoverability bounds theoretical guarantees"
    - "analysis of sparse mimo radar"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"

Connected through hyperedge HE-6 (induced by source paper "compressive deconvolution in medical ultrasound imaging"):
  with members:
    - "relaxing tight frame condition in parallel proximal methods for signal restoration"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "sampling and recovery of pulse streams"

Connected through hyperedge HE-7 (induced by source paper "focus fourier based coded ultrasound"):
  with members:
    - "compressed beamforming in ultrasound imaging"

Connected through hyperedge HE-8 (induced by source paper "reconstruction of enhanced ultrasound images from compressed measurements using simultaneous direction method of multipliers"):
  with members:
    - "compressive deconvolution in medical ultrasound imaging"
    - "compressive deconvolution in random mask imaging"
    - "relaxing tight frame condition in parallel proximal methods for signal restoration"
    - "fast and efficient compressive sensing using structurally random matrices"

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)