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 "infrared and visible image fusion with resnet and zero phase component analysis"):
  Abstract: Abstract   In image fusion approaches, feature extraction and processing are key tasks, and the fusion performance is directly affected by the different features and processing methods undertaken. However, most of deep learning-based methods use deep features directly without them. This leads to the fusion performance degradation in some cases. To solve these drawbacks, in our paper, a deep features and zero-phase component analysis (ZCA) based novel fusion framework is proposed. Firstly, the residual network (ResNet) is used to extract deep features from source images. Then ZCA and       l    1      -norm are utilized to normalize the deep features and obtain initial weight maps. The final weight maps are obtained by employing a soft-max operation in association with the initial weight maps. Finally, the fused image is reconstructed using a weighted-averaging strategy. Compared with the existing fusion methods, experimental results demonstrate that the proposed framework achieves better performance in both objective assessment and visual quality. The code of our fusion algorithm is available at  https://github.com/hli1221/imagefusion_resnet50 .

Includes members:
  Member "densefuse a fusion approach to infrared and visible images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion")
  Member "avatar net multi scale zero shot style transfer by feature decoration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks")
  Member "infrared and visible image fusion using a deep learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion")
  Member "infrared and visible image fusion using latent low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vifb a visible and infrared image fusion benchmark")
  Member "image fusion with cosparse analysis operator":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion")
  Member "multi focus image fusion using dictionary learning and low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion")
  Member "multi style generative network for real time transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "domain adaptive few shot learning")
  Member "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion")

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)