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: "illuminant spectra based source separation using flash photography"
  Abstract: Real-world lighting often consists of multiple illuminants with different spectra. Separating and manipulating these illuminants in post-process is a challenging problem that requires either significant manual input or calibrated scene geometry and lighting. In this work, we leverage a flash/no-flash image pair to analyze and edit scene illuminants based on their spectral differences. We derive a novel physics-based relationship between color variations in the observed flash/no-flash intensities and the spectra and surface shading corresponding to individual scene illuminants. Our technique uses this constraint to automatically separate an image into constituent images lit by each illuminant. This separation can be used to support applications like white balancing, lighting editing, and RGB photometric stereo, where we demonstrate results that outperform state-of-the-art techniques on a wide range of images.

Connected through hyperedge HE-1 (induced by source paper "a dataset of multi illumination images in the wild"):
  with members:
    - "photoshape photorealistic materials for large scale shape collections"
    - "learning to predict indoor illumination from a single image"
    - "deep outdoor illumination estimation"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "natural illumination from multiple materials using deep learning"
    - "convolutional color constancy"
    - "u net convolutional networks for biomedical image segmentation"
    - "material recognition in the wild with the materials in context database"

Connected through hyperedge HE-2 (induced by source paper "learning to separate multiple illuminants in a single image"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning intrinsic image decomposition from watching the world"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "learning to predict indoor illumination from a single image"
    - "semantic scene completion from a single depth image"
    - "perceptual losses for real time style transfer and super resolution"
    - "deep outdoor illumination estimation"
    - "image to image translation with conditional adversarial networks"

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)