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: "compressive image recovery using recurrent generative model"
  Abstract: Reconstruction of signals from compressively sensed measurements is an ill-posed problem. In this paper, we leverage the recurrent generative model, RIDE, as an image prior for compressive image reconstruction. Recurrent networks can model long-range dependencies in images and hence are suitable to handle global multiplexing in reconstruction from compressive imaging. We perform MAP inference with RIDE using back-propagation to the inputs and projected gradient method. We propose an entropy thresholding based approach for preserving texture in images well. Our approach shows superior reconstructions compared to recent global reconstruction approaches like D-AMP and TVAL3 on both simulated and real data.

Connected through hyperedge HE-1 (induced by source paper "joint chromatic and polarimetric demosaicing via sparse coding"):
  with members:
    - "residual dense network for image super resolution"
    - "learning to see in the dark"
    - "accurate image super resolution using very deep convolutional networks"
    - "image deblurring and super resolution by adaptive sparse domain selection and adaptive regularization"

Connected through hyperedge HE-2 (induced by source paper "solving inverse computational imaging problems using deep pixel level prior"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "learning proximal operators using denoising networks for regularizing inverse imaging problems"
    - "lensless computational imaging through deep learning"
    - "one network to solve them all solving linear inverse problems using deep projection models"
    - "learning deep cnn denoiser prior for image restoration"
    - "pixelnet representation of the pixels by the pixels and for the pixels"
    - "learning a variational network for reconstruction of accelerated mri data"

Connected through hyperedge HE-3 (induced by source paper "phase retrieval for fourier ptychography under varying amount of measurements"):
  with members:
    - "phase recovery and holographic image reconstruction using deep learning in neural networks"
    - "deep image prior"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "one network to solve them all solving linear inverse problems using deep projection models"):
  with members:
    - "pixel recursive super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "plug play generative networks conditional iterative generation of images in latent space"
    - "improved techniques for training gans"
    - "mode regularized generative adversarial networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "a deep learning approach to structured signal recovery"

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)