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 "iterative residual cnns for burst photography applications"):
  Abstract: Modern inexpensive imaging sensors suffer from inherent hardware constraints which often result in captured images of poor quality. Among the most common ways to deal with such limitations is to rely on burst photography, which nowadays acts as the backbone of all modern smartphone imaging applications. In this work, we focus on the fact that every frame of a burst sequence can be accurately described by a forward (physical) model. This in turn allows us to restore a single image of higher quality from a sequence of low quality images as the solution of an optimization problem. Inspired by an extension of the gradient descent method that can handle non-smooth functions, namely the proximal gradient descent, and modern deep learning techniques, we propose a convolutional iterative network with a transparent architecture. Our network, uses a burst of low quality image frames and is able to produce an output of higher image quality recovering fine details which are not distinguishable in any of the original burst frames. We focus both on the burst photography pipeline as a whole, i.e. burst demosaicking and denoising, as well as on the traditional Gaussian denoising task. The developed method demonstrates consistent state-of-the art performance across the two tasks and as opposed to other recent deep learning approaches does not have any inherent restrictions either to the number of frames or their ordering.

Includes members:
  Member "iterative joint image demosaicking and denoising using a residual denoising network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pixel adaptive filtering units")
  Member "deep image demosaicking using a cascade of convolutional residual denoising networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural imaging pipelines the scourge or hope of forensics")
  Member "deep burst denoising":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "burst denoising of dark images")
  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 "memnet a persistent memory network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "universal denoising networks a novel cnn architecture for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gated texture cnn for efficient and configurable image denoising")
  Member "burst denoising with kernel prediction networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "regularized adaptation for stable and efficient continuous level learning on image processing networks")

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)