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: "darn a deep adversial residual network for intrinsic image decomposition"
  Abstract: We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components. Our contributions are based on a new fully convolutional neural network that estimates absolute albedo and shading jointly. Our solution relies on a single end-to-end deep sequence of residual blocks and a perceptually-motivated metric formed by two adversarially trained discriminators. As opposed to classical intrinsic image decomposition work, it is fully data-driven, hence does not require any physical priors like shading smoothness or albedo sparsity, nor does it rely on geometric information such as depth. Compared to recent deep learning techniques, we simplify the architecture, making it easier to build and train, and constrain it to generate a valid and reversible decomposition. We rediscuss and augment the set of quantitative metrics so as to account for the more challenging recovery of non scale-invariant quantities. We train and demonstrate our architecture on the publicly available MPI Sintel dataset and its intrinsic image decomposition, show attenuated overfitting issues and discuss generalizability to other data. Results show that our work outperforms the state of the art deep algorithms both on the qualitative and quantitative aspect.

Connected through hyperedge HE-1 (induced by source paper "an approximate shading model with detail decomposition for object relighting"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "neural face editing with intrinsic image disentangling"
    - "learning detailed face reconstruction from a single image"
    - "learning diverse image colorization"
    - "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"
    - "depth extraction from video using non parametric sampling"

Connected through hyperedge HE-2 (induced by source paper "intrinsic image transformation via scale space decomposition"):
  with members:
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "neural face editing with intrinsic image disentangling"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "revisiting deep intrinsic image decompositions"
    - "cvae gan fine grained image generation through asymmetric training"
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-3 (induced by source paper "joint learning of intrinsic images and semantic segmentation"):
  with members:
    - "analyzing modular cnn architectures for joint depth prediction and semantic segmentation"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "joint semantic segmentation and depth estimation with deep convolutional networks"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-4 (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"
    - "shading annotations in the wild"
    - "semantic scene completion from a single depth image"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "reflectance adaptive filtering improves intrinsic image estimation"):
  with members:
    - "single image depth perception in the wild"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "u net convolutional networks for biomedical image segmentation"
    - "multi scale context aggregation by dilated convolutions"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "caffe convolutional architecture for fast feature embedding"

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)