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: "natural illumination from multiple materials using deep learning"
  Abstract: Recovering natural illumination from a single Low-Dynamic Range (LDR) image is a challenging task. To remedy this situation we exploit two properties often found in everyday images. First, images rarely show a single material, but rather multiple ones that all reflect the same illumination. However, the appearance of each material is observed only for some surface orientations, not all. Second, parts of the illumination are often directly observed in the background, without being affected by reflection. Typically, this directly observed part of the illumination is even smaller. We propose a deep Convolutional Neural Network (CNN) that combines prior knowledge about the statistics of illumination and reflectance with an input that makes explicit use of these two observations. Our approach maps multiple partial LDR material observations represented as reflectance maps and a background image to a spherical High-Dynamic Range (HDR) illumination map. For training and testing we propose a new data set comprising of synthetic and real images with multiple materials observed under the same illumination. Qualitative and quantitative evidence shows how both multi-material and using a background are essential to improve illumination estimations.

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"
    - "illuminant spectra based source separation using flash photography"
    - "deep outdoor illumination estimation"
    - "physically based rendering for indoor scene understanding using convolutional neural networks"
    - "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 "joint material and illumination estimation from photo sets in the wild"):
  with members:
    - "live intrinsic material estimation"
    - "material editing using a physically based rendering network"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "novel views of objects from a single image"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "designing deep networks for surface normal estimation"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

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)