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: "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
  Abstract: We present a novel deep learning architecture for fusing static multi-exposure images. Current multi-exposure fusion (MEF) approaches use hand-crafted features to fuse input sequence. However, the weak hand-crafted representations are not robust to varying input conditions. Moreover, they perform poorly for extreme exposure image pairs. Thus, it is highly desirable to have a method that is robust to varying input conditions and capable of handling extreme exposure without artifacts. Deep representations have known to be robust to input conditions and have shown phenomenal performance in a supervised setting. However, the stumbling block in using deep learning for MEF was the lack of sufficient training data and an oracle to provide the ground-truth for supervision. To address the above issues, we have gathered a large dataset of multi-exposure image stacks for training and to circumvent the need for ground truth images, we propose an unsupervised deep learning framework for MEF utilizing a no-reference quality metric as loss function. The proposed approach uses a novel CNN architecture trained to learn the fusion operation without reference ground truth image. The model fuses a set of common low level features extracted from each image to generate artifact-free perceptually pleasing results. We perform extensive quantitative and qualitative evaluation and show that the proposed technique outperforms existing state-of-the-art approaches for a variety of natural images.

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"
    - "infrared and visible image fusion using a deep learning framework"

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 a deep learning framework"
    - "infrared and visible image fusion using latent low rank representation"
    - "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 "tbc net a real time detector for infrared small target detection using semantic constraint"):
  with members:
    - "segmentation aware image denoising without knowing true segmentation"
    - "iterative crowd counting"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "when image denoising meets high level vision tasks a deep learning approach"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "dilated residual networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-4 (induced by source paper "deep convolutional neural network for multi modal image restoration and fusion"):
  with members:
    - "densefuse a fusion approach to infrared and visible images"
    - "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"
    - "enhanced deep residual networks for single image super resolution"

Connected through hyperedge HE-5 (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 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"
    - "detail revealing deep video super resolution"

Connected through hyperedge HE-6 (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"
    - "infrared and visible image fusion using a deep learning framework"
    - "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-7 (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"
    - "infrared and visible image fusion using a deep learning framework"
    - "non local neural networks"
    - "squeeze and excitation networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-8 (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"
    - "infrared and visible image fusion using a deep learning framework"
    - "squeeze and excitation networks"
    - "perceptual losses for real time style transfer and super resolution"

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)