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 "reciprocal translation between sar and optical remote sensing images with cascaded residual adversarial networks"):
  Abstract: Despite the advantages of all-weather and all-day high-resolution imaging, synthetic aperture radar (SAR) images are much less viewed and used by general people because human vision is not adapted to microwave scattering phenomenon. However, expert interpreters can be trained by comparing side-by-side SAR and optical images to learn the mapping rules from SAR to optical. This paper attempts to develop machine intelligence that are trainable with large-volume co-registered SAR and optical images to translate SAR image to optical version for assisted SAR image interpretation. Reciprocal SAR-Optical image translation is a challenging task because it is raw data translation between two physically very different sensing modalities. This paper proposes a novel reciprocal adversarial network scheme where cascaded residual connections and hybrid L1-GAN loss are employed. It is trained and tested on both spaceborne GF-3 and airborne UAVSAR images. Results are presented for datasets of different resolutions and polarizations and compared with other state-of-the-art methods. The FID is used to quantitatively evaluate the translation performance. The possibility of unsupervised learning with unpaired SAR and optical images is also explored. Results show that the proposed translation network works well under many scenarios and it could potentially be used for assisted SAR interpretation.

Includes members:
  Member "multi temporal sentinel 1 and 2 data fusion for optical image simulation":
    also connected through other hyperedges:
      (none)
  Member "generating high quality visible images from sar images using cnns":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the sen1 2 dataset for deep learning in sar optical data fusion")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "toward multimodal image to image translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "photographic image synthesis with cascaded refinement networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "fader networks manipulating images by sliding attributes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "unsupervised cross domain image generation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "learning from simulated and unsupervised images through adversarial training":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)