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 "super resolution via image adapted denoising cnns incorporating external and internal learning"):
  Abstract: While deep neural networks exhibit state-of-the-art results in the task of image super-resolution (SR) with a fixed known acquisition process (e.g., a bicubic downscaling kernel), they experience a huge performance loss when the real observation model mismatches the one used in training. Recently, two different techniques suggested to mitigate this deficiency, i.e., enjoy the advantages of deep learning without being restricted by the training phase. The first one follows the plug-and-play (P&P) approach that solves general inverse problems (e.g., SR) by using Gaussian denoisers for handling the prior term in model-based optimization schemes. The second builds on internal recurrence of information inside a single image, and trains a super-resolver network at test time on examples synthesized from the low-resolution image. Our letter incorporates these two independent strategies, enjoying the impressive generalization capabilities of deep learning, captured by the first, and further improving it through internal learning at test time. First, we apply a recent P&P strategy to SR. Then, we show how it may become image-adaptive in test time. This technique outperforms the above two strategies on popular datasets and gives better results than other state-of-the-art methods in practical cases where the observation model is inexact or unknown in advance.

Includes members:
  Member "deep learning for single image super resolution a brief review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "class aware fully convolutional gaussian and poisson denoising":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gated texture cnn for efficient and configurable image denoising")
  Member "recovering realistic texture in image super resolution by deep spatial feature transform":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "regularization by denoising clarifications and new interpretations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bilinear recovery using adaptive vector amp")
  Member "the 2018 pirm challenge on perceptual image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perceptual image super resolution with progressive adversarial network")
  Member "image super resolution using very deep residual channel attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "an online plug and play algorithm for regularized image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "plug and play methods provably converge with properly trained denoisers")
  Member "a fully progressive approach to single image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fine grained visual classification via progressive multi granularity training of jigsaw patches")

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)