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 latent low rank representation"
  Abstract: Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we propose a novel image fusion method based on latent low-rank representation(LatLRR) which is simple and effective. This is the first time that LatLRR is introduced to image fusion. Firstly, the source images are decomposed into low-rank parts(global structure) and saliency parts(local structure) by LatLRR. Then, the low-rank parts are fused by weighted-average strategy, and the saliency parts are simply fused by sum strategy. Finally, the fused image is obtained by combining the fused low-rank part and the fused saliency part. Compared with other fusion methods experimentally, the proposed method 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 "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"
    - "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-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"
    - "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-3 (induced by source paper "multi focus image fusion based on similarity characteristics"):
  with members:
    - "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking"

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)