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 "what else can fool deep learning addressing color constancy errors on deep neural network performance"):
  Abstract: There is active research targeting local image manipulations that can fool deep neural networks (DNNs) into producing incorrect results. This paper examines a type of global image manipulation that can produce similar adverse effects. Specifically, we explore how strong color casts caused by incorrectly applied computational color constancy - referred to as white balance (WB) in photography - negatively impact the performance of DNNs targeting image segmentation and classification. In addition, we discuss how existing image augmentation methods used to improve the robustness of DNNs are not well suited for modeling WB errors. To address this problem, a novel augmentation method is proposed that can emulate accurate color constancy degradation. We also explore pre-processing training and testing images with a recent WB correction algorithm to reduce the effects of incorrectly white-balanced images. We examine both augmentation and pre-processing strategies on different datasets and demonstrate notable improvements on the CIFAR-10, CIFAR-100, and ADE20K datasets.

Includes members:
  Member "sensor independent illumination estimation for dnn models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "modeling camera effects to improve deep vision for real and synthetic data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a multi hypothesis approach to color constancy")
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "batch instance normalization for adaptively style invariant neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "augmented cyclic consistency regularization for unpaired image to image translation")
  Member "unprocessing images for learned raw denoising":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning enriched features for real image restoration and enhancement")
  Member "threat of adversarial attacks on deep learning in computer vision a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "random erasing data augmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on feature normalization and data augmentation")

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)