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: "joint face hallucination and deblurring via structure generation and detail enhancement"
  Abstract: We address the problem of restoring a high-resolution face image from a blurry low-resolution input. This problem is difficult as super-resolution and deblurring need to be tackled simultaneously. Moreover, existing algorithms cannot handle face images well as low-resolution face images do not have much texture which is especially critical for deblurring. In this paper, we propose an effective algorithm by utilizing the domain-specific knowledge of human faces to recover high-quality faces. We first propose a facial component guided deep Convolutional Neural Network (CNN) to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image. However, the CNN based method cannot handle image details well. We further develop a novel exemplar-based detail enhancement algorithm via facial component matching. Extensive experiments show that the proposed method outperforms the state-of-the-art algorithms both quantitatively and qualitatively.

Connected through hyperedge HE-1 (induced by source paper "facial expression restoration based on improved graph convolutional networks"):
  with members:
    - "deep adaptive attention for joint facial action unit detection and face alignment"
    - "rain streak removal for single image via kernel guided cnn"
    - "generative adversarial network based image super resolution using perceptual content losses"
    - "learning a discriminative prior for blind image deblurring"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-2 (induced by source paper "deblurring face images using uncertainty guided multi stream semantic networks"):
  with members:
    - "deep semantic face deblurring"
    - "smoothed dilated convolutions for improved dense prediction"
    - "multi style generative network for real time transfer"
    - "instance normalization the missing ingredient for fast stylization"
    - "perceptual losses for real time style transfer and super resolution"
    - "cnn for license plate motion deblurring"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "attentive ct lesion detection using deep pyramid inference with multi scale booster"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "squeeze and excitation networks"
    - "deeplesion automated deep mining categorization and detection of significant radiology image findings using large scale clinical lesion annotations"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "fast preprocessing for robust face sketch synthesis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

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)