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: "compressed beamforming in ultrasound imaging"
  Abstract: Emerging sonography techniques often require increasing the number of transducer elements involved in the imaging process. Consequently, larger amounts of data must be acquired and processed. The significant growth in the amounts of data affects both machinery size and power consumption. Within the classical sampling framework, state of the art systems reduce processing rates by exploiting the bandpass bandwidth of the detected signals. It has been recently shown, that a much more significant sample-rate reduction may be obtained, by treating ultrasound signals within the Finite Rate of Innovation framework. These ideas follow the spirit of Xampling, which combines classic methods from sampling theory with recent developments in Compressed Sensing. Applying such low-rate sampling schemes to individual transducer elements, which detect energy reflected from biological tissues, is limited by the noisy nature of the signals. This often results in erroneous parameter extraction, bringing forward the need to enhance the SNR of the low-rate samples. In our work, we achieve SNR enhancement, by beamforming the sub-Nyquist samples obtained from multiple elements. We refer to this process as “compressed beamforming”. Applying it to cardiac ultrasound data, we successfully image macroscopic perturbations, while achieving a nearly eightfold reduction in sample-rate, compared to standard techniques.

Connected through hyperedge HE-1 (induced by source paper "serial quantization for sparse time sequences"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "information theoretic and algorithmic thresholds for group testing"
    - "efficient data collection over multiple access wireless sensors network"
    - "efficient nonrandom construction and decoding of non adaptive group testing"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "distributed quantization for measurement of correlated sparse sources over noisy channels"
    - "distributed compressed sensing for static and time varying networks"

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 "serial quantization for representing sparse signals"):
  with members:
    - "learning distributions from their samples under communication constraints"
    - "hardware limited task based quantization"
    - "efficient data collection over multiple access wireless sensors network"
    - "quantization of compressive samples with stable and robust recovery"
    - "quantization and compressive sensing"
    - "spatial compressive sensing for mimo radar"
    - "group testing algorithms bounds and simulations"
    - "quantized iterative hard thresholding bridging 1 bit and high resolution quantized compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "sparse convolutional beamforming for ultrasound imaging"):
  with members:
    - "fourier domain beamforming the path to compressed ultrasound imaging"

Connected through hyperedge HE-5 (induced by source paper "efficient b mode ultrasound image reconstruction from sub sampled rf data using deep learning"):
  with members:
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "k space deep learning for accelerated mri"
    - "image reconstruction by domain transform manifold 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"
    - "deep learning for accelerated ultrasound imaging"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-6 (induced by source paper "deep learning in rf sub sampled b mode ultrasound imaging"):
  with members:
    - "deep residual learning for accelerated mri using magnitude and phase networks"
    - "k space deep learning for accelerated mri"
    - "image reconstruction by domain transform manifold 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"
    - "deep learning for accelerated ultrasound imaging"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep convolutional neural network for inverse problems in imaging"

Connected through hyperedge HE-7 (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"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "xampling at the rate of innovation"
    - "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-8 (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"
    - "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"

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)