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: "densefuse a fusion approach to infrared and visible images"
  Abstract: In this paper, we present a novel deep learning architecture for infrared and visible images fusion problems. In contrast to conventional convolutional networks, our encoding network is combined with convolutional layers, a fusion layer, and dense block in which the output of each layer is connected to every other layer. We attempt to use this architecture to get more useful features from source images in the encoding process, and two fusion layers (fusion strategies) are designed to fuse these features. Finally, the fused image is reconstructed by a decoder. Compared with existing fusion methods, the proposed fusion method achieves the state-of-the-art performance in objective and subjective assessment.

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"
    - "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"
    - "infrared and visible image fusion using a deep learning framework"
    - "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"
    - "rgb t object tracking benchmark and baseline"
    - "infrared and visible image fusion using a deep learning framework"
    - "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 "deep convolutional neural network for multi modal image restoration and fusion"):
  with members:
    - "feedback network for image super resolution"
    - "deformable kernel networks for joint image filtering"
    - "fast end to end trainable guided filter"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "co occurrence filter"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-4 (induced by source paper "infrared and visible image fusion with resnet and zero phase component analysis"):
  with members:
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "infrared and visible image fusion using a deep learning framework"
    - "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-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"
    - "a cross modal image fusion theory guided by human visual characteristics"
    - "hallucinated iqa no reference image quality assessment via adversarial learning"
    - "infrared and visible image fusion using a deep learning framework"
    - "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:
    - "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"
    - "infrared and visible image fusion using a deep learning framework"
    - "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 "low rank discriminative least squares regression for image classification"):
  with members:
    - "locality constraint dictionary learning with support vector for pattern classification"
    - "joint group feature selection and discriminative filter learning for robust visual object tracking"
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "robust recovery of subspace structures by low rank representation"

Connected through hyperedge HE-8 (induced by source paper "a robust non linear and feature selection image fusion theory"):
  with members:
    - "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"
    - "infrared and visible image fusion using a deep learning framework"
    - "squeeze and excitation networks"

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)