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: "a unified approach of multi scale deep and hand crafted features for defocus estimation"
  Abstract: In this paper, we introduce robust and synergetic hand-crafted features and a simple but efficient deep feature from a convolutional neural network (CNN) architecture for defocus estimation. This paper systematically analyzes the effectiveness of different features, and shows how each feature can compensate for the weaknesses of other features when they are concatenated. For a full defocus map estimation, we extract image patches on strong edges sparsely, after which we use them for deep and hand-crafted feature extraction. In order to reduce the degree of patch-scale dependency, we also propose a multi-scale patch extraction strategy. A sparse defocus map is generated using a neural network classifier followed by a probability-joint bilateral filter. The final defocus map is obtained from the sparse defocus map with guidance from an edge-preserving filtered input image. Experimental results show that our algorithm is superior to state-of-the-art algorithms in terms of defocus estimation. Our work can be used for applications such as segmentation, blur magnification, all-in-focus image generation, and 3-D estimation.

Connected through hyperedge HE-1 (induced by source paper "synthetic defocus and look ahead autofocus for casual videography"):
  with members:
    - "sg fcn a motion and memory based deep learning model for video saliency detection"
    - "deeply supervised salient object detection with short connections"
    - "synthetic depth of field with a single camera mobile phone"
    - "deeplens shallow depth of field from a single image"
    - "revisiting video saliency a large scale benchmark and a new model"
    - "looking to listen at the cocktail party a speaker independent audio visual model for speech separation"
    - "scanner efficient video analysis at scale"
    - "tracking emerges by colorizing videos"

Connected through hyperedge HE-2 (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"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "rankiqa learning from rankings for no reference image quality assessment"

Connected through hyperedge HE-3 (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"
    - "spatially varying blur detection based on multiscale fused and sorted transform coefficients of gradient magnitudes"
    - "sparsity invariant cnns"
    - "joint transmission map estimation and dehazing using deep networks"

Connected through hyperedge HE-4 (induced by source paper "weakly and self supervised learning for content aware deep image retargeting"):
  with members:
    - "deep saliency with encoded low level distance map and high level features"
    - "perceptual losses for real time style transfer and super resolution"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "semantic image inpainting with perceptual and contextual losses"
    - "accurate image super resolution using very deep convolutional networks"
    - "fast r cnn"

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)