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: "infrared and visible image fusion using a deep learning framework"
  Abstract: In recent years, deep learning has become a very active research tool which is used in many image processing fields. In this paper, we propose an effective image fusion method using a deep learning framework to generate a single image which contains all the features from infrared and visible images. First, the source images are decomposed into base parts and detail content. Then the base parts are fused by weighted-averaging. For the detail content, we use a deep learning network to extract multi-layer features. Using these features, we use  $l_{1}$ -norm and weighted-average strategy to generate several candidates of the fused detail content. Once we get these candidates, the max selection strategy is used to get the final fused detail content. Finally, the fused image will be reconstructed by combining the fused base part and the detail content. The experimental results demonstrate that our proposed method achieves state-of-the-art performance in both objective assessment and visual quality. The Code of our fusion method is available at https://github.com/exceptionLi/imagefusion_deeplearning.

Connected through hyperedge HE-1 (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion"):
  with members:
    - "an accelerated correlation filter tracker"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "infrared and visible image fusion with resnet and zero phase component analysis"
    - "densefuse a fusion approach to infrared and visible images"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "rgb t object tracking benchmark and baseline"
    - "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability"
    - "image fusion with cosparse analysis operator"

Connected through hyperedge HE-2 (induced by source paper "vifb a visible and infrared image fusion benchmark"):
  with members:
    - "infrared and visible image fusion with resnet and zero phase component analysis"
    - "densefuse a fusion approach to infrared and visible images"
    - "rgb t object tracking benchmark and baseline"
    - "infrared and visible image fusion using latent low rank representation"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "a novel performance evaluation methodology for single target trackers"
    - "medical image fusion a survey of the state of the art"

Connected through hyperedge HE-3 (induced by source paper "infrared and visible image fusion with resnet and zero phase component analysis"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "infrared and visible image fusion using latent low rank representation"
    - "image fusion with cosparse analysis operator"
    - "multi focus image fusion using dictionary learning and low rank representation"
    - "multi style generative network for real time transfer"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "detail revealing deep video super resolution"

Connected through hyperedge HE-4 (induced by source paper "fast and efficient zero learning image fusion"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "hardware efficient guided image filtering for multi label problem"
    - "fast end to end trainable guided filter"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "cross modal image fusion theory guided by subjective visual attention"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "densefuse a fusion approach to infrared and visible images"
    - "a cross modal image fusion theory guided by human visual characteristics"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "non local neural networks"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "squeeze and excitation networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-6 (induced by source paper "a cross modal image fusion theory guided by human visual characteristics"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "fast and efficient zero learning image fusion"
    - "image super resolution using very deep residual channel attention networks"
    - "infrared and visible image fusion using a novel deep decomposition method"
    - "end to end multi task learning with attention"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "squeeze and excitation networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-7 (induced by source paper "a robust non linear and feature selection image fusion theory"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "fast and efficient zero learning image fusion"
    - "image super resolution using very deep residual channel attention networks"
    - "infrared and visible image fusion using a novel deep decomposition method"
    - "squeeze and excitation networks"

Connected through hyperedge HE-8 (induced by source paper "infrared and visible image fusion using latent low rank representation"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "image fusion with cosparse analysis operator"
    - "multi focus image fusion using dictionary learning and low rank representation"

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.CV(Computer Vision and Pattern Recognition)