human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sub nyquist sar via fourier domain range doppler processing"):
  Abstract: Conventional synthetic aperture radar (SAR) systems are limited in their ability to satisfy the increasing requirement for improved spatial resolution and wider coverage. The demand for high resolution requires high sampling rates, while coverage is limited by the pulse repetition frequency. Consequently, sampling rate reduction is of high practical value in SAR imaging. In this paper, we introduce a new algorithm, equivalent to the well-known range-Doppler method, to process SAR data using the Fourier series coefficients of the raw signals. We then demonstrate how to exploit the algorithm features to reduce sampling rate in both range and azimuth axes and process the signals at sub-Nyquist rates, by using compressive sensing (CS) tools. In particular, we demonstrate recovery of an image using only a portion of the received signal’s bandwidth and also while dropping a large percentage of the transmitted pulses. The complementary pulses may be used to capture other scenes within the same coherent processing interval. In addition, we propose exploiting the ability to reconstruct the image from narrow bands in order to dynamically adapt the transmitted waveform energy to vacant spectral bands, paving the way to cognitive SAR. The proposed recovery algorithms form a new CS-SAR imaging method that can be applied to high-resolution SAR data acquired at sub-Nyquist rates in range and azimuth. The performance of our method is assessed using simulated and real data sets. Finally, our approach is implemented in hardware using a previously suggested Xampling radar prototype.

Includes members:
  Member "fast compressed sensing sar imaging based on approximated observation":
    also connected through other hyperedges:
      (none)
  Member "recovery of sparse matrices via matrix sketching":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "localized compression applying convolutional neural networks to compressed images")
  Member "fourier domain beamforming the path to compressed ultrasound imaging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sparse convolutional beamforming for ultrasound imaging")
  Member "xampling at the rate of innovation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint transmit and receive filter optimization for sub nyquist delay doppler estimation")
  Member "compressed beamforming in ultrasound imaging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "serial quantization for sparse time sequences")
  Member "sub nyquist radar via doppler focusing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "multichannel sampling of pulse streams at the rate of innovation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "high resolution compressed sensing radar using difference set codes")
  Member "sub nyquist sampling bridging theory and practice":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on recoverability of finite traces of square summable sequences")

Question: Please tell me which class the source paper 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)