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 "fast bayesian uncertainty estimation of batch normalized single image super resolution network"):
  Abstract: In recent years, deep convolutional neural network (CNN) has achieved unprecedented success in image super-resolution (SR) task. But the black-box nature of the neural network and due to its lack of transparency, it is hard to trust the outcome. In this regards, we introduce a Bayesian approach for uncertainty estimation in super-resolution network. We generate Monte Carlo (MC) samples from a posterior distribution by using batch mean and variance as a stochastic parameter in the batch-normalization layer during test time. Those MC samples not only reconstruct the image from its low-resolution counterpart but also provides a confidence map of reconstruction which will be very impactful for practical use. We also introduce a faster approach for estimating the uncertainty, and it can be useful for real-time applications. We validate our results using standard datasets for performance analysis and also for different domain-specific super-resolution task. We also estimate uncertainty quality using standard statistical metrics and also provides a qualitative evaluation of uncertainty for SR applications.

Includes members:
  Member "deep learning for image super resolution a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "inherent brain segmentation quality control from fully convnet monte carlo sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks")
  Member "learning to decode 7t like mr image reconstruction from 3t mr images":
    also connected through other hyperedges:
      (none)
  Member "residual dense network for image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "seeing through fog without seeing fog deep multimodal sensor fusion in unseen adverse weather")
  Member "densely connected pyramid dehazing network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "fully convolutional model for variable bit length and lossy high density compression of mammograms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "ultracompression framework for high density compression of ultrasound volumes using physics modeling deep neural networks")
  Member "sketch based manga retrieval using manga109 dataset":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "closed loop matters dual regression networks for single image super resolution")
  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")

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)