human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "solving inverse computational imaging problems using deep pixel level prior"):
  Abstract: Generative models based on deep neural networks are quite powerful in modelling natural image statistics. In particular, deep auto-regressive models provide state of the art performance, in terms of log likelihood scores, by modelling tractable densities over the image manifold. In this work, we employ a learned deep auto-regressive model as data prior for solving different inverse problems in computational imaging. We demonstrate how our approach can reconstruct images which have better pixel-level consistencies, as compared to the existing deep auto-encoder based approaches. We also show how randomly dropping the update of some pixels in every iteration helps in a better image reconstruction. We test our approach on three computational imaging setups: Single Pixel Camera, LiSens and FlatCam with real and simulated measurements. We obtain better reconstructions than the state-of-the-art methods for these problems, in terms of both perceptual quality and quantitative metrics such as PSNR and SSIM.

Includes members:
  Member "dr2 net deep residual reconstruction network for image compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "improving robustness of deep learning based image reconstruction")
  Member "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive density estimation for generative models")
  Member "learning proximal operators using denoising networks for regularizing inverse imaging problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "lensless computational imaging through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "blending diverse physical priors with neural networks")
  Member "one network to solve them all solving linear inverse problems using deep projection models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "proximal splitting networks for image restoration")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sparse weight activation training")
  Member "pixelnet representation of the pixels by the pixels and for the pixels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "mti net multi scale task interaction networks for multi task learning")
  Member "learning a variational network for reconstruction of accelerated mri data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")

Question: Please tell me which class the source paper 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)