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 "watch your up convolution cnn based generative deep neural networks are failing to reproduce spectral distributions"):
  Abstract: Generative convolutional deep neural networks, e.g. popular GAN architectures, are relying on convolution based up-sampling methods to produce non-scalar outputs like images or video sequences. In this paper, we show that common up-sampling methods, i.e. known as up-convolution or transposed convolution, are causing the inability of such models to reproduce spectral distributions of natural training data correctly. This effect is independent of the underlying architecture and we show that it can be used to easily detect generated data like deepfakes with up to 100% accuracy on public benchmarks. #R##N#To overcome this drawback of current generative models, we propose to add a novel spectral regularization term to the training optimization objective. We show that this approach not only allows to train spectral consistent GANs that are avoiding high frequency errors. Also, we show that a correct approximation of the frequency spectrum has positive effects on the training stability and output quality of generative networks.

Includes members:
  Member "faceforensics learning to detect manipulated facial images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "celeb df a large scale challenging dataset for deepfake forensics")
  Member "stabilizing gans with octave convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectral graph attention network")
  Member "figr few shot image generation with reptile":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "matchinggan matching based few shot image generation")
  Member "semi few shot attribute translation":
    also connected through other hyperedges:
      (none)
  Member "band limited training and inference for convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a fourier perspective on model robustness in computer vision")
  Member "unmasking deepfakes with simple features":
    also connected through other hyperedges:
      (none)
  Member "a fourier perspective on model robustness in computer vision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "affinity and diversity quantifying mechanisms of data augmentation")
  Member "attributing fake images to gans learning and analyzing gan fingerprints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "leveraging frequency analysis for deep fake image recognition")

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)