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: "learning warped guidance for blind face restoration"
  Abstract: This paper studies the problem of blind face restoration from an unconstrained blurry, noisy, low-resolution, or compressed image (i.e., degraded observation). For better recovery of fine facial details, we modify the problem setting by taking both the degraded observation and a high-quality guided image of the same identity as input to our guided face restoration network (GFRNet). However, the degraded observation and guided image generally are different in pose, illumination and expression, thereby making plain CNNs (e.g., U-Net) fail to recover fine and identity-aware facial details. To tackle this issue, our GFRNet model includes both a warping subnetwork (WarpNet) and a reconstruction subnetwork (RecNet). The WarpNet is introduced to predict flow field for warping the guided image to correct pose and expression (i.e., warped guidance), while the RecNet takes the degraded observation and warped guidance as input to produce the restoration result. Due to that the ground-truth flow field is unavailable, landmark loss together with total variation regularization are incorporated to guide the learning of WarpNet. Furthermore, to make the model applicable to blind restoration, our GFRNet is trained on the synthetic data with versatile settings on blur kernel, noise level, downsampling scale factor, and JPEG quality factor. Experiments show that our GFRNet not only performs favorably against the state-of-the-art image and face restoration methods, but also generates visually photo-realistic results on real degraded facial images.

Connected through hyperedge HE-1 (induced by source paper "stgan a unified selective transfer network for arbitrary image attribute editing"):
  with members:
    - "modular generative adversarial networks"
    - "unsupervised multi domain image translation with domain specific encoders decoders"
    - "combogan unrestrained scalability for image domain translation"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-2 (induced by source paper "exemplar guided face image super resolution without facial landmarks"):
  with members:
    - "the 2018 pirm challenge on perceptual image super resolution"
    - "super identity convolutional neural network for face hallucination"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "attention aware face hallucination via deep reinforcement learning"
    - "enhanced deep residual networks for single image super resolution"
    - "improved training of wasserstein gans"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "face image reflection removal"):
  with members:
    - "video enhancement with task oriented flow"
    - "single image reflection separation with perceptual losses"
    - "deep semantic face deblurring"
    - "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-4 (induced by source paper "learning symmetry consistent deep cnns for face completion"):
  with members:
    - "identity preserving face completion for large ocular region occlusion"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "shift net image inpainting via deep feature rearrangement"
    - "generative image inpainting with contextual attention"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "generative face completion"
    - "contextual based image inpainting infer match and translate"

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)