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 novel deep decomposition method"
  Abstract: Infrared and visible image fusion is an important problem in image fusion tasks which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we propose an effective image fusion framework using a novel deep decomposition method which based on Latent Low-Rank Representation(LatLRR). And this decomposition method is also named DDLatLRR. Firstly, the LatLRR is utilized to learn a project matrix which used to extract salient features. Then, the base part and multi-level detail parts are obtained by DDLatLRR. With adaptive fusion strategies, the fused base part and the fused detail parts are reconstructed. Finally, the fused image is obtained by combine the fused base part and the detail parts. Compared with other fusion methods experimentally, the proposed algorithm has better fusion performance than state-of-the-art fusion methods in both subjective and objective evaluation. The Code of our fusion method is available at this https URL

Connected through hyperedge HE-1 (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"
    - "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-2 (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 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)