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: "unsupervised domain specific deblurring via disentangled representations"
  Abstract: Image deblurring aims to restore the latent sharp images from the corresponding blurred ones. In this paper, we present an unsupervised method for domain-specific single-image deblurring based on disentangled representations. The disentanglement is achieved by splitting the content and blur features in a blurred image using content encoders and blur encoders. We enforce a KL divergence loss to regularize the distribution range of extracted blur attributes such that little content information is contained. Meanwhile, to handle the unpaired training data, a blurring branch and the cycle-consistency loss are added to guarantee that the content structures of the deblurred results match the original images. We also add an adversarial loss on deblurred results to generate visually realistic images and a perceptual loss to further mitigate the artifacts. We perform extensive experiments on the tasks of face and text deblurring using both synthetic datasets and real images, and achieve improved results compared to recent state-of-the-art deblurring methods.

Connected through hyperedge HE-1 (induced by source paper "blur noise and compression robust generative adversarial networks"):
  with members:
    - "analyzing and improving the image quality of stylegan"
    - "noise robust generative adversarial networks"
    - "high quality self supervised deep image denoising"
    - "noise2self blind denoising by self supervision"
    - "diversity sensitive conditional generative adversarial networks"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "toward convolutional blind denoising of real photographs"
    - "noise2void learning denoising from single noisy images"

Connected through hyperedge HE-2 (induced by source paper "unsupervised multi domain multimodal image to image translation with explicit domain constrained disentanglement"):
  with members:
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "a unified feature disentangler for multi domain image translation and manipulation"
    - "learning to adapt structured output space for semantic segmentation"
    - "multimodal unsupervised image to image translation"
    - "crafting a toolchain for image restoration by deep reinforcement learning"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-3 (induced by source paper "domain adaptive crowd counting via inter domain features segregation and gaussian prior reconstruction"):
  with members:
    - "c 3 framework an open source pytorch code for crowd counting"
    - "cross dataset person re identification via unsupervised pose disentanglement and adaptation"
    - "all about structure adapting structural information across domains for boosting semantic segmentation"
    - "pushing the frontiers of unconstrained crowd counting new dataset and benchmark method"
    - "learning from synthetic data for crowd counting in the wild"
    - "crowd counting with deep structured scale integration network"
    - "generalizing semi supervised generative adversarial networks to regression using feature contrasting"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"

Connected through hyperedge HE-4 (induced by source paper "deraincyclegan an attention guided unsupervised benchmark for single image deraining and rainmaking"):
  with members:
    - "spatial attentive single image deraining with a high quality real rain dataset"
    - "progressive image deraining networks a better and simpler baseline"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "cycle dehaze enhanced cyclegan for single image dehazing"
    - "semi supervised transfer learning for image rain removal"
    - "density aware single image de raining using a multi stream dense network"
    - "drit diverse image to image translation via disentangled representations"
    - "clearing the skies a deep network architecture for single image rain removal"

Connected through hyperedge HE-5 (induced by source paper "atfacegan single face image restoration and recognition from atmospheric turbulence"):
  with members:
    - "subsampled turbulence removal network"
    - "deep semantic face deblurring"
    - "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-6 (induced by source paper "illumination adaptive person re identification"):
  with members:
    - "exploring disentangled feature representation beyond face identification"
    - "fd gan pose guided feature distilling gan for robust person re identification"
    - "learning to see in the dark"
    - "image to image translation for cross domain disentanglement"
    - "drit diverse image to image translation via disentangled representations"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "pose driven deep convolutional model for person re identification"
    - "unlabeled samples generated by gan improve the person re identification baseline in vitro"

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)