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: "deep semantic face deblurring"
  Abstract: In this paper, we present an effective and efficient face deblurring algorithm by exploiting semantic cues via deep convolutional neural networks (CNNs). As face images are highly structured and share several key semantic components (e.g., eyes and mouths), the semantic information of a face provides a strong prior for restoration. As such, we propose to incorporate global semantic priors as input and impose local structure losses to regularize the output within a multi-scale deep CNN. We train the network with perceptual and adversarial losses to generate photo-realistic results and develop an incremental training strategy to handle random blur kernels in the wild. Quantitative and qualitative evaluations demonstrate that the proposed face deblurring algorithm restores sharp images with more facial details and performs favorably against state-of-the-art methods in terms of restoration quality, face recognition and execution speed.

Connected through hyperedge HE-1 (induced by source paper "human aware motion deblurring"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "a trilateral weighted sparse coding scheme for real world image denoising"
    - "gated fusion network for single image dehazing"
    - "scale recurrent network for deep image deblurring"
    - "external prior guided internal prior learning for real world noisy image denoising"
    - "image correction via deep reciprocating hdr transformation"
    - "multi channel weighted nuclear norm minimization for real color image denoising"
    - "learning blind motion deblurring"

Connected through hyperedge HE-2 (induced by source paper "handling noise in image deblurring via joint learning"):
  with members:
    - "efficient blind deblurring under high noise levels"
    - "gated fusion network for joint image deblurring and super resolution"
    - "scale recurrent network for deep image deblurring"
    - "learning to deblur images with exemplars"
    - "toward convolutional blind denoising of real photographs"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "deep multi scale convolutional neural network for dynamic scene deblurring"

Connected through hyperedge HE-3 (induced by source paper "segmentation aware image denoising without knowing true segmentation"):
  with members:
    - "single image deraining a comprehensive benchmark analysis"
    - "ug 2 track 2 a collective benchmark effort for evaluating and advancing image understanding in poor visibility environments"
    - "enhance visual recognition under adverse conditions via deep networks"
    - "bridging the gap between computational photography and visual recognition"
    - "yolov3 an incremental improvement"
    - "toward convolutional blind denoising of real photographs"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "connecting image denoising and high level vision tasks via deep learning"

Connected through hyperedge HE-4 (induced by source paper "on low resolution face recognition in the wild comparisons and new techniques"):
  with members:
    - "generative adversarial networks an overview"
    - "additive margin softmax for face verification"
    - "face super resolution through wasserstein gans"
    - "deep face deblurring"
    - "sphereface deep hypersphere embedding for face recognition"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "l2 constrained softmax loss for discriminative face verification"

Connected through hyperedge HE-5 (induced by source paper "unsupervised domain specific deblurring via disentangled representations"):
  with members:
    - "an experimental evaluation of covariates effects on unconstrained face verification"
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "exploring disentangled feature representation beyond face identification"
    - "towards open set identity preserving face synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "drit diverse image to image translation via disentangled representations"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "face image reflection removal"):
  with members:
    - "video enhancement with task oriented flow"
    - "single image reflection separation with perceptual losses"
    - "learning warped guidance for blind face restoration"
    - "crrn multi scale guided concurrent reflection removal network"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "is second order information helpful for large scale visual recognition"

Connected through hyperedge HE-7 (induced by source paper "atfacegan single face image restoration and recognition from atmospheric turbulence"):
  with members:
    - "unsupervised domain specific deblurring via disentangled representations"
    - "subsampled turbulence removal network"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "deep face deblurring"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "cross age lfw a database for studying cross age face recognition in unconstrained environments"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-8 (induced by source paper "matching thermal to visible face images using a semantic guided generative adversarial network"):
  with members:
    - "polarimetric thermal to visible face verification via attribute preserved synthesis"
    - "the gan landscape losses architectures regularization and normalization"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "thermal to visible synthesis of face images using multiple regions"
    - "deep cross polarimetric thermal to visible face recognition"
    - "mobilefacenets efficient cnns for accurate real time face verification on mobile devices"
    - "additive margin softmax for face verification"
    - "macro micro adversarial network for human parsing"

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)