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 "a multi hypothesis approach to color constancy"):
  Abstract: Contemporary approaches frame the color constancy problem as learning camera specific illuminant mappings. While high accuracy can be achieved on camera specific data, these models depend on camera spectral sensitivity and typically exhibit poor generalisation to new devices. Additionally, regression methods produce point estimates that do not explicitly account for potential ambiguities among plausible illuminant solutions, due to the ill-posed nature of the problem. We propose a Bayesian framework that naturally handles color constancy ambiguity via a multi-hypothesis strategy. Firstly, we select a set of candidate scene illuminants in a data-driven fashion and apply them to a target image to generate of set of corrected images. Secondly, we estimate, for each corrected image, the likelihood of the light source being achromatic using a camera-agnostic CNN. Finally, our method explicitly learns a final illumination estimate from the generated posterior probability distribution. Our likelihood estimator learns to answer a camera-agnostic question and thus enables effective multi-camera training by disentangling illuminant estimation from the supervised learning task. We extensively evaluate our proposed approach and additionally set a benchmark for novel sensor generalisation without re-training. Our method provides state-of-the-art accuracy on multiple public datasets (up to 11% median angular error improvement) while maintaining real-time execution.

Includes members:
  Member "sensor independent illumination estimation for dnn models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what else can fool deep learning addressing color constancy errors on deep neural network performance")
  Member "color cerberus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fast fourier color constancy and grayness index for ispa illumination estimation challenge")
  Member "what else can fool deep learning addressing color constancy errors on deep neural network performance":
    also connected through other hyperedges:
      (none)
  Member "fast fourier color constancy and grayness index for ispa illumination estimation challenge":
    also connected through other hyperedges:
      (none)
  Member "convolutional mean a simple convolutional neural network for illuminant estimation":
    also connected through other hyperedges:
      (none)
  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 "what else can fool deep learning addressing color constancy errors on deep neural network performance")
  Member "a mixed classification regression framework for 3d pose estimation from 2d images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semi local 3d lane detection and uncertainty estimation")
  Member "approaching the computational color constancy as a classification problem through deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "local area transform for cross modality correspondence matching and deep scene 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)