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 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.

Includes members:
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "denoising prior driven deep neural network for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast dense residual network enhancing global dense feature flow for text recognition")
  Member "deep image demosaicking using a cascade of convolutional residual denoising networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural imaging pipelines the scourge or hope of forensics")
  Member "deep residual network for joint demosaicing and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "zoom to learn learn to zoom")
  Member "learning deep cnn denoiser prior for image restoration":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse weight activation training")
  Member "ista net iterative shrinkage thresholding algorithm inspired deep network for image compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trainable ista for sparse signal recovery")
  Member "beyond a gaussian denoiser residual learning of deep cnn for image denoising":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "gated texture cnn for efficient and configurable image 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)