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: "image fusion with cosparse analysis operator"
  Abstract: The letter addresses the image fusion problem, where multiple images captured with different focus distances are to be combined into a higher quality all-in-focus image. Most current approaches for image fusion strongly rely on the unrealistic noise-free assumption used during the image acquisition, and then yield limited fusion robustness. In our approach, we formulate the multifocus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multifocus images. Based on this model, we propose an analysis operator learning, and define a novel fusion function to generate an all-in-focus image. Experimental evaluations confirm the effectiveness of the proposed fusion approach both visually and quantitatively, and show that our approach outperforms the state-of-the-art fusion methods.

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 "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"
    - "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-3 (induced by source paper "densefuse a fusion approach to infrared and visible images"):
  with members:
    - "normalized total gradient a new measure for multispectral image registration"
    - "multi focus image fusion using dictionary learning and low rank representation"
    - "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "iterated extended kalman smoother based variable splitting for l_1 regularized state estimation"):
  with members:
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "doubly robust smoothing of dynamical processes via outlier sparsity constraints"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-5 (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"
    - "infrared and visible image fusion using a deep learning framework"
    - "multi focus image fusion using dictionary learning and low rank representation"

Connected through hyperedge HE-6 (induced by source paper "infrared and visible image fusion using a deep learning framework"):
  with members:
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "multi focus image fusion using sparse representation and coupled dictionary learning"):
  with members:
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "convex denoising using non convex tight frame regularization"

Connected through hyperedge HE-8 (induced by source paper "multi focus image fusion via coupled sparse representation and dictionary learning"):
  with members:
    - "plug and play admm for image restoration fixed point convergence and applications"
    - "exploiting statistical dependencies in sparse representations for signal recovery"

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)