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 "class specific poisson denoising by patch based importance sampling"):
  Abstract: In this paper, we address the problem of recovering images degraded by Poisson noise, where the image is known to belong to a specific class. In the proposed method, a dataset of clean patches from images of the class of interest is clustered using multivariate Gaussian distributions. In order to recover the noisy image, each noisy patch is assigned to one of these distributions, and the corresponding minimum mean squared error (MMSE) estimate is obtained. We propose to use a self-normalized importance sampling approach, which is a method of the Monte-Carlo family, for the both determining the most likely distribution and approximating the MMSE estimate of the clean patch. Experimental results shows that our proposed method outperforms other methods for Poisson denoising at a low SNR regime.

Includes members:
  Member "image restoration and reconstruction using variable splitting and class adapted image priors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "poisson inverse problems by the plug and play scheme":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "adaptive image denoising by targeted databases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised fast adaptation for denoising via meta learning")
  Member "sparsity based poisson denoising with dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")
  Member "poisson noise reduction with non local pca":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional dictionary learning based auto encoders for natural exponential family distributions")

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)