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: "shading annotations in the wild"
  Abstract: Understanding shading effects in images is critical for a variety of vision and graphics problems, including intrinsic image decomposition, shadow removal, image relighting, and inverse rendering. As is the case with other vision tasks, machine learning is a promising approach to understanding shading - but there is little ground truth shading data available for real-world images. We introduce Shading Annotations in the Wild (SAW), a new large-scale, public dataset of shading annotations in indoor scenes, comprised of multiple forms of shading judgments obtained via crowdsourcing, along with shading annotations automatically generated from RGB-D imagery. We use this data to train a convolutional neural network to predict per-pixel shading information in an image. We demonstrate the value of our data and network in an application to intrinsic images, where we can reduce decomposition artifacts produced by existing algorithms. Our database is available at this http URL

Connected through hyperedge HE-1 (induced by source paper "shadingnet image intrinsics by fine grained shading decomposition"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "interiornet mega scale multi sensor photo realistic indoor scenes dataset"
    - "learning intrinsic image decomposition from watching the world"
    - "joint learning of intrinsic images and semantic segmentation"
    - "unsupervised deep single image intrinsic decomposition using illumination varying image sequences"
    - "intrinsic image transformation via scale space decomposition"
    - "squeeze and excitation networks"
    - "self supervised intrinsic image decomposition"

Connected through hyperedge HE-2 (induced by source paper "unsupervised learning for intrinsic image decomposition from a single image"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning intrinsic image decomposition from watching the world"
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "unsupervised image to image translation networks"
    - "self supervised intrinsic image decomposition"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-3 (induced by source paper "semantic hierarchical priors for intrinsic image decomposition"):
  with members:
    - "deep hybrid real and synthetic training for intrinsic decomposition"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning intrinsic image decomposition from watching the world"
    - "reflectance adaptive filtering improves intrinsic image estimation"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "how transferable are features in deep neural networks"
    - "depth transfer depth extraction from video using non parametric sampling"

Connected through hyperedge HE-4 (induced by source paper "structural decompositions for end to end relighting"):
  with members:
    - "neural inverse rendering of an indoor scene from a single image"
    - "synthetic depth of field with a single camera mobile phone"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "learning to predict indoor illumination from a single image"
    - "neural face editing with intrinsic image disentangling"
    - "deep outdoor illumination estimation"
    - "reflectance adaptive filtering improves intrinsic image estimation"

Connected through hyperedge HE-5 (induced by source paper "leveraging multi view image sets for unsupervised intrinsic image decomposition and highlight separation"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "learning intrinsic image decomposition from watching the world"
    - "faces as lighting probes via unsupervised deep highlight extraction"
    - "self supervised intrinsic image decomposition"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"

Connected through hyperedge HE-6 (induced by source paper "cgintrinsics better intrinsic image decomposition through physically based rendering"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "neural face editing with intrinsic image disentangling"
    - "self supervised intrinsic image decomposition"
    - "playing for benchmarks"
    - "semantic scene completion from a single depth image"
    - "virtual worlds as proxy for multi object tracking analysis"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "image to image translation with conditional adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "learning intrinsic image decomposition from watching the world"):
  with members:
    - "self supervised intrinsic image decomposition"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "image to image translation with conditional adversarial networks"
    - "pyramid scene parsing network"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "u net convolutional networks for biomedical image segmentation"
    - "shapenet an information rich 3d model repository"

Connected through hyperedge HE-8 (induced by source paper "unsupervised deep single image intrinsic decomposition using illumination varying image sequences"):
  with members:
    - "learning intrinsic image decomposition from watching the world"
    - "self supervised intrinsic image decomposition"
    - "semantic scene completion from a single depth image"
    - "darn a deep adversial residual network for intrinsic image decomposition"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "shapenet an information rich 3d model repository"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "adam a method for stochastic optimization"

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)