human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sensor independent illumination estimation for dnn models"
  Abstract: While modern deep neural networks (DNNs) achieve state-of-the-art results for illuminant estimation, it is currently necessary to train a separate DNN for each type of camera sensor. This means when a camera manufacturer uses a new sensor, it is necessary to retrain an existing DNN model with training images captured by the new sensor. This paper addresses this problem by introducing a novel sensor-independent illuminant estimation framework. Our method learns a sensor-independent working space that can be used to canonicalize the RGB values of any arbitrary camera sensor. Our learned space retains the linear property of the original sensor raw-RGB space and allows unseen camera sensors to be used on a single DNN model trained on this working space. We demonstrate the effectiveness of this approach on several different camera sensors and show it provides performance on par with state-of-the-art methods that were trained per sensor.

Connected through hyperedge HE-1 (induced by source paper "a multi hypothesis approach to color constancy"):
  with members:
    - "color cerberus"
    - "what else can fool deep learning addressing color constancy errors on deep neural network performance"
    - "fast fourier color constancy and grayness index for ispa illumination estimation challenge"
    - "convolutional mean a simple convolutional neural network for illuminant estimation"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "a mixed classification regression framework for 3d pose estimation from 2d images"
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "unsupervised learning for color constancy"

Connected through hyperedge HE-2 (induced by source paper "what else can fool deep learning addressing color constancy errors on deep neural network performance"):
  with members:
    - "spatially transformed adversarial examples"
    - "modeling camera effects to improve deep vision for real and synthetic data"
    - "autoaugment learning augmentation policies from data"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unprocessing images for learned raw denoising"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "random erasing data augmentation"
    - "modelling the scene dependent imaging in cameras with a deep neural network"

Question: Please tell me which class the center node 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)