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: "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
  Abstract: The detection of spatially-varying blur without having any information about the blur type is a challenging task. In this paper, we propose a novel effective approach to address the blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings. Our approach computes blur detection maps based on a novel High-frequency multiscale Fusion and Sort Transform (HiFST) of gradient magnitudes. The evaluations of the proposed approach on a diverse set of blurry images with different blur types, levels, and contents demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.

Connected through hyperedge HE-1 (induced by source paper "self supervised blur detection from synthetically blurred scenes"):
  with members:
    - "single image depth estimation trained via depth from defocus cues"
    - "learning to adapt structured output space for semantic segmentation"
    - "self supervised learning of geometrically stable features through probabilistic introspection"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "self supervised feature learning by learning to spot artifacts"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "a unified approach of multi scale deep and hand crafted features for defocus estimation"
    - "rankiqa learning from rankings for no reference image quality assessment"

Connected through hyperedge HE-2 (induced by source paper "deepblindness fast blindness map estimation and blindness type classification for outdoor scene from single color image"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning to synthesize motion blur"
    - "deep depth from defocus how can defocus blur improve 3d estimation using dense neural networks"
    - "haze density estimation via modeling of scattering coefficients of iso depth regions"
    - "deeplidar deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image"
    - "a unified approach of multi scale deep and hand crafted features for defocus estimation"
    - "sparsity invariant cnns"
    - "joint transmission map estimation and dehazing using deep networks"

Connected through hyperedge HE-3 (induced by source paper "sharpness aware low dose ct denoising using conditional generative adversarial network"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "image de raining using a conditional generative adversarial network"
    - "ct image denoising with perceptive deep neural networks"
    - "the pose knows video forecasting by generating pose futures"

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)