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 "adaptive quantile sparse image aquasi prior for inverse imaging problems"):
  Abstract: Inverse problems play a central role for many classical computer vision and image processing tasks. A key challenge in solving an inverse problem is to find an appropriate prior to convert an ill-posed problem into a well-posed task. Many of the existing priors, like total variation, are based on ad-hoc assumptions that have difficulties to represent the actual distribution of natural images. #R##N#In this work, we propose the Adaptive Quantile Sparse Image (AQuaSI) prior. It is based on a quantile filter, can be used as a joint filter on guidance data, and be readily plugged into a wide range of numerical optimization algorithms. We demonstrate the efficacy of the proposed prior in joint RGB/depth upsampling, on RGB/NIR image restoration, and in a comparison with related regularization by denoising approaches.

Includes members:
  Member "deep image prior":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "semantic pyramid for image generation")
  Member "learning proximal operators using denoising networks for regularizing inverse imaging problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "zero order reverse filtering":
    also connected through other hyperedges:
      (none)
  Member "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse weight activation training")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "joint image filtering with deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "concurrently extrapolating and interpolating networks for continuous model generation")
  Member "the little engine that could regularization by denoising red":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ada lista learned solvers adaptive to varying models")

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)