human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mdlatlrr a novel decomposition method for infrared and visible image fusion"):
  Abstract: Image decomposition is crucial for many image processing tasks, as it allows to extract salient features from source images. A good image decomposition method could lead to a better performance, especially in image fusion tasks. We propose a multi-level image decomposition method based on latent low-rank representation(LatLRR), which is called MDLatLRR. This decomposition method is applicable to many image processing fields. In this paper, we focus on the image fusion task. We build a novel image fusion framework based on MDLatLRR which is used to decompose source images into detail parts(salient features) and base parts. A nuclear-norm based fusion strategy is used to fuse the detail parts and the base parts are fused by an averaging strategy. Compared with other state-of-the-art fusion methods, the proposed algorithm exhibits better fusion performance in both subjective and objective evaluation.

Includes members:
  Member "an accelerated correlation filter tracker":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adaptive distraction context aware tracking based on correlation filter")
  Member "joint group feature selection and discriminative filter learning for robust visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi drone based single object tracking with agent sharing network")
  Member "infrared and visible image fusion with resnet and zero phase component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "vifb a visible and infrared image fusion benchmark")
  Member "densefuse a fusion approach to infrared and visible images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vifb a visible and infrared image fusion benchmark")
  Member "learning adaptive discriminative correlation filters via temporal consistency preserving spatial feature selection for robust visual object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "siamese attentional keypoint network for high performance visual tracking")
  Member "rgb t object tracking benchmark and baseline":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "heatnet bridging the day night domain gap in semantic segmentation with thermal images")
  Member "super resolution for hyperspectral and multispectral image fusion accounting for seasonal spectral variability":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep generative endmember modeling an application to unsupervised spectral unmixing")
  Member "infrared and visible image fusion using a deep learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "vifb a visible and infrared image fusion benchmark")

Question: Please tell me which class the source paper 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)