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 "external patch based image restoration using importance sampling"):
  Abstract: This paper introduces a new approach to patch-based image restoration based on external datasets and importance sampling. The Minimum Mean Squared Error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The new method, which can be interpreted as a generalization of the external non-local means (NLM), uses self-normalized importance sampling to efficiently approximate the MMSE estimates. The use of self-normalized importance sampling endows the proposed method with great flexibility, namely regarding the statistical properties of the measurement noise. The effectiveness of the proposed method is shown in a series of experiments using both generic large-scale and class-specific external datasets.

Includes members:
  Member "poisson image denoising using best linear prediction a post processing framework":
    also connected through other hyperedges:
      (none)
  Member "class specific image denoising using importance sampling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "class aware fully convolutional gaussian and poisson denoising")
  Member "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "transfer learning from synthetic to real noise denoising with adaptive instance normalization")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "deep class aware denoising":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "xunit learning a spatial activation function for efficient image restoration")
  Member "deep convolutional denoising of low light images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving electron micrograph signal to noise with an atrous convolutional encoder decoder")
  Member "image restoration and reconstruction using variable splitting and class adapted image priors":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "deep gaussian conditional random field network a model based deep network for discriminative denoising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "class aware fully convolutional gaussian and poisson denoising")

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)