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 "quantum spectral analysis frequency in time with applications to signal and image processing"):
  Abstract: A quantum time-dependent spectrum analysis, or simply, quantum spectral analysis (QSA) is presented in this work, and it's based on Schrodinger equation, which is a partial differential equation that describes how the quantum state of a non-relativistic physical system changes with time. In classic world is named frequency in time (FIT), which is presented here in opposition and as a complement of traditional spectral analysis frequency-dependent based on Fourier theory. Besides, FIT is a metric, which assesses the impact of the flanks of a signal on its frequency spectrum, which is not taken into account by Fourier theory and even less in real time. Even more, and unlike all derived tools from Fourier Theory (i.e., continuous, discrete, fast, short-time, fractional and quantum Fourier Transform, as well as, Gabor) FIT has the following advantages: a) compact support with excellent energy output treatment, b) low computational cost, O(N) for signals and O(N 2) for images, c) it does not have phase uncertainties (indeterminate phase for magnitude = 0) as Discrete and Fast Fourier Transform (DFT, FFT, respectively), d) among others. In fact, FIT constitutes one side of a triangle (which from now on is closed) and it consists of the original signal in time, spectral analysis based on Fourier Theory and FIT. Thus a toolbox is completed, which it is essential for all applications of Digital Signal Processing (DSP) and Digital Image Processing (DIP); and, even, in the latter, FAT allows edge detection (which is called flank detection in case of signals), denoising, despeckling, compression, and superresolution of still images. Such applications include signals intelligence and imagery intelligence. On the other hand, we will present other DIP tools, which are also derived from the Schrodinger equation. Besides, we discuss several examples for spectral analysis, edge detection, denoising, despeckling, compression and superresolution in a set of experimental results in an important section on Applications and Simulations, respectively. Finally, we finish this work with special section dedicated to Conclusions.

Includes members:
  Member "enhanced directional smoothing algorithm for edge preserving smoothing of synthetic aperture radar images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "systholic boolean orthonormalizer network in wavelet domain for sar image despeckling")
  Member "kalman s shrinkage for wavelet based despeckling of sar images":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "systholic boolean orthonormalizer network in wavelet domain for sar image despeckling")
  Member "denoising based on wavelets and deblurring via self organizing map for synthetic aperture radar images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "systholic boolean orthonormalizer network in wavelet domain for sar image despeckling")
  Member "fuzzy thresholding in wavelet domain for speckle reduction in synthetic aperture radar images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "systholic boolean orthonormalizer network in wavelet domain for sar image despeckling")
  Member "fast cosine transform to increase speed up and efficiency of karhunen loeve transform for lossy image compression":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantum spectral analysis bandwidth at time")
  Member "neural shrinkage for wavelet based sar despeckling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "systholic boolean orthonormalizer network in wavelet domain for sar image despeckling")
  Member "new wavelet based superresolution algorithm for speckle reduction in sar images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "systholic boolean orthonormalizer network in wavelet domain for sar image despeckling")
  Member "rule of three for superresolution of still images with applications to compression and denoising":
    also connected through other hyperedges:
      (none)

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.CV(Computer Vision and Pattern Recognition)