human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "iterative joint image demosaicking and denoising using a residual denoising network"
  Abstract: Modern digital cameras rely on the sequential execution of separate image processing steps to produce realistic images. The first two steps are usually related to denoising and demosaicking, where the former aims to reduce noise from the sensor and the latter converts a series of light intensity readings to color images. Modern approaches try to jointly solve these problems, i.e., joint denoising-demosaicking, which is an inherently ill-posed problem given that two-thirds of the intensity information is missing and the rest is perturbed by noise. While there are several machine learning systems that have been recently introduced to solve this problem, the majority of them rely on generic network architectures, which do not explicitly consider the physical image model. In this paper, we propose a novel algorithm that is inspired by powerful classical image regularization methods, large-scale optimization, and deep learning techniques. Consequently, our derived iterative optimization algorithm, which involves a trainable denoising network, has a transparent and clear interpretation compared with other black-box data driven approaches. Our extensive experimentation line demonstrates that our proposed method outperforms any previous approaches for both noisy and noise-free data across many different datasets. This improvement in reconstruction quality is attributed to the rigorous derivation of an iterative solution and the principled way we design our denoising network architecture, which as a result requires fewer trainable parameters than the current state-of-the-art solution, and furthermore can be efficiently trained by using a significantly smaller number of training data than existing deep demosaicking networks.

Connected through hyperedge HE-1 (induced by source paper "pixel adaptive filtering units"):
  with members:
    - "pixel adaptive convolutional neural networks"
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "neural nearest neighbors networks"
    - "segmentation aware convolutional networks using local attention masks"
    - "blade filter learning for general purpose computational photography"
    - "convolutional networks with adaptive inference graphs"
    - "enhanced deep residual networks for single image super resolution"
    - "burst denoising with kernel prediction networks"

Connected through hyperedge HE-2 (induced by source paper "revisiting non local sparse models for image restoration"):
  with members:
    - "residual non local attention networks for image restoration"
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "neural nearest neighbors networks"
    - "non local recurrent network for image restoration"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-3 (induced by source paper "iterative residual cnns for burst photography applications"):
  with members:
    - "deep image demosaicking using a cascade of convolutional residual denoising networks"
    - "deep burst denoising"
    - "learning deep cnn denoiser prior for image restoration"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"
    - "universal denoising networks a novel cnn architecture for image denoising"
    - "burst denoising with kernel prediction networks"
    - "the little engine that could regularization by denoising red"

Question: Please tell me which class the center node 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)