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 "pretraining image encoders without reconstruction via feature prediction loss"):
  Abstract: This work investigates three different loss functions for autoencoder-based pretraining of image encoders: The commonly used reconstruction loss, the more recently introduced perceptual similarity loss, and a feature prediction loss proposed here; the latter turning out to be the most efficient choice. Former work shows that predictions based on embeddings generated by image autoencoders can be improved by training with perceptual loss. So far the autoencoders trained with perceptual loss networks implemented an explicit comparison of the original and reconstructed images using the loss network. However, given such a loss network we show that there is no need for the timeconsuming task of decoding the entire image. Instead, we propose to decode the features of the loss network, hence the name "feature prediction loss". To evaluate this method we compare six different procedures for training image encoders based on pixel-wise, perceptual similarity, and feature prediction loss. The embedding-based prediction results show that encoders trained with feature prediction loss is as good or better than those trained with the other two losses. Additionally, the encoder is significantly faster to train using feature prediction loss in comparison to the other losses. The method implementation used in this work is available online: this https URL

Includes members:
  Member "improving image autoencoder embeddings with perceptual loss":
    also connected through other hyperedges:
      (none)
  Member "mixmatch a holistic approach to semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "domain adaptive ensemble learning")
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "the unreasonable effectiveness of deep features as a perceptual metric":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "beyond the pixel wise loss for topology aware delineation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving image autoencoder embeddings with perceptual loss")
  Member "perceptual losses for real time style transfer and super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "generating images with perceptual similarity metrics based on deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "object centric image generation from layouts")
  Member "on the quantitative analysis of decoder based generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "towards gan benchmarks which require generalization")

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)