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 face deblurring"
  Abstract: Blind deblurring consists a long studied task, however the outcomes of generic methods are not effective in real world blurred images. Domain-specific methods for deblurring targeted object categories, e.g. text or faces, frequently outperform their generic counterparts, hence they are attracting an increasing amount of attention. In this work, we develop such a domain-specific method to tackle deblurring of human faces, henceforth referred to as face deblurring. Studying faces is of tremendous significance in computer vision, however face deblurring has yet to demonstrate some convincing results. This can be partly attributed to the combination of i) poor texture and ii) highly structure shape that yield the contour/gradient priors (that are typically used) sub-optimal. In our work instead of making assumptions over the prior, we adopt a learning approach by inserting weak supervision that exploits the well-documented structure of the face. Namely, we utilise a deep network to perform the deblurring and employ a face alignment technique to pre-process each face. We additionally surpass the requirement of the deep network for thousands training samples, by introducing an efficient framework that allows the generation of a large dataset. We utilised this framework to create 2MF2, a dataset of over two million frames. We conducted experiments with real world blurred facial images and report that our method returns a result close to the sharp natural latent image.

Connected through hyperedge HE-1 (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"
    - "deep semantic face deblurring"
    - "face super resolution through wasserstein gans"
    - "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-2 (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"
    - "deep semantic face deblurring"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "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-3 (induced by source paper "fab a robust facial landmark detection framework for motion blurred videos"):
  with members:
    - "look at boundary a boundary aware face alignment algorithm"
    - "motion deblurring of faces"
    - "style aggregated network for facial landmark detection"
    - "deep semantic face deblurring"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "online video deblurring via dynamic temporal blending network"
    - "learning blind motion deblurring"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"

Connected through hyperedge HE-4 (induced by source paper "motion deblurring of faces"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "a comprehensive performance evaluation of deformable face tracking in the wild"
    - "deep generative filter for motion deblurring"
    - "learning blind motion deblurring"
    - "motion deblurring in the wild"
    - "attention aware face hallucination via deep reinforcement learning"
    - "how far are we from solving the 2d 3d face alignment problem and a dataset of 230 000 3d facial landmarks"
    - "deblurgan blind motion deblurring using conditional adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "learning warped guidance for blind face restoration"):
  with members:
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "vggface2 a dataset for recognising faces across pose and age"
    - "generative face completion"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "motion deblurring in the wild"
    - "attention aware face hallucination via deep reinforcement learning"
    - "deblurgan blind motion deblurring using conditional adversarial networks"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-6 (induced by source paper "face recognition in low quality images a survey"):
  with members:
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "coupled learning for facial deblur"
    - "super identity convolutional neural network for face hallucination"
    - "deep cnn denoiser and multi layer neighbor component embedding for face hallucination"
    - "low resolution face recognition"
    - "deep semantic face deblurring"
    - "surveillance face recognition challenge"
    - "face super resolution through wasserstein gans"

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)