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: "learning high dynamic range from outdoor panoramas"
  Abstract: Outdoor lighting has extremely high dynamic range. This makes the process of capturing outdoor environment maps notoriously challenging since special equipment must be used. In this work, we propose an alternative approach. We first capture lighting with a regular, LDR omnidirectional camera, and aim to recover the HDR after the fact via a novel, learning-based inverse tonemapping method. We propose a deep autoencoder framework which regresses linear, high dynamic range data from non-linear, saturated, low dynamic range panoramas. We validate our method through a wide set of experiments on synthetic data, as well as on a novel dataset of real photographs with ground truth. Our approach finds applications in a variety of settings, ranging from outdoor light capture to image matching.

Connected through hyperedge HE-1 (induced by source paper "deeplight learning illumination for unconstrained mobile mixed reality"):
  with members:
    - "learning to estimate indoor lighting from 3d objects"
    - "generative image inpainting with contextual attention"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "learning to predict indoor illumination from a single image"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"
    - "joint material and illumination estimation from photo sets in the wild"

Connected through hyperedge HE-2 (induced by source paper "fhdr hdr image reconstruction from a single ldr image using feedback network"):
  with members:
    - "feedback network for image super resolution"
    - "deep chain hdri reconstructing a high dynamic range image from a single low dynamic range image"
    - "expandnet a deep convolutional neural network for high dynamic range expansion from low dynamic range content"
    - "residual dense network for image super resolution"
    - "image correction via deep reciprocating hdr transformation"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "perceptual losses for real time style transfer and super resolution"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deep sky modeling for single image outdoor lighting estimation"):
  with members:
    - "all weather deep outdoor lighting estimation"
    - "taskonomy disentangling task transfer learning"
    - "learning a predictable and generative vector representation for objects"
    - "deep outdoor illumination estimation"
    - "deep residual learning for image recognition"
    - "deep reflectance maps"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "all weather deep outdoor lighting estimation"):
  with members:
    - "deep sky modeling for single image outdoor lighting estimation"
    - "deep outdoor illumination estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep reflectance maps"
    - "fast and accurate deep network learning by exponential linear units elus"

Connected through hyperedge HE-5 (induced by source paper "omnidepth dense depth estimation for indoors spherical panoramas"):
  with members:
    - "restricted deformable convolution based road scene semantic segmentation using surround view cameras"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "artistic style transfer for videos and spherical images"
    - "light weight pixel context encoders for image inpainting"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "the adobeindoornav dataset towards deep reinforcement learning based real world indoor robot visual navigation"
    - "active convolution learning the shape of convolution for image classification"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-6 (induced by source paper "image correction via deep reciprocating hdr transformation"):
  with members:
    - "stylizing face images via multiple exemplars"
    - "msr net low light image enhancement using deep convolutional network"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "video deblurring via semantic segmentation and pixel wise non linear kernel"
    - "learning deep cnn denoiser prior for image restoration"
    - "segflow joint learning for video object segmentation and optical flow"
    - "memnet a persistent memory network for image restoration"
    - "crest convolutional residual learning for visual tracking"

Connected through hyperedge HE-7 (induced by source paper "expandnet a deep convolutional neural network for high dynamic range expansion from low dynamic range content"):
  with members:
    - "self normalizing neural networks"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "deep feature consistent deep image transformations downscaling decolorization and hdr tone mapping"
    - "is the deconvolution layer the same as a convolutional layer"
    - "deep outdoor illumination estimation"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "deep chain hdri reconstructing a high dynamic range image from a single low dynamic range image"):
  with members:
    - "self normalizing neural networks"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "fast and accurate image super resolution by deep cnn with skip connection and network in network"
    - "learning deep cnn denoiser prior for image restoration"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "densely connected convolutional networks"
    - "colorful image colorization"
    - "accurate image super resolution using very deep convolutional 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)