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: "direct intrinsics learning albedo shading decomposition by convolutional regression"
  Abstract: We introduce a new approach to intrinsic image decomposition, the task of decomposing a single image into albedo and shading components. Our strategy, which we term direct intrinsics, is to learn a convolutional neural network (CNN) that directly predicts output albedo and shading channels from an input RGB image patch. Direct intrinsics is a departure from classical techniques for intrinsic image decomposition, which typically rely on physically-motivated priors and graph-based inference algorithms. #R##N#The large-scale synthetic ground-truth of the MPI Sintel dataset plays a key role in training direct intrinsics. We demonstrate results on both the synthetic images of Sintel and the real images of the classic MIT intrinsic image dataset. On Sintel, direct intrinsics, using only RGB input, outperforms all prior work, including methods that rely on RGB+Depth input. Direct intrinsics also generalizes across modalities; it produces quite reasonable decompositions on the real images of the MIT dataset. Our results indicate that the marriage of CNNs with synthetic training data may be a powerful new technique for tackling classic problems in computer vision.

Connected through hyperedge HE-1 (induced by source paper "lifting autoencoders unsupervised learning of a fully disentangled 3d morphable model using deep non rigid structure from motion"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deep interpretable non rigid structure from motion"
    - "face alignment in full pose range a 3d total solution"
    - "visual object networks image generation with disentangled 3d representation"
    - "learning category specific mesh reconstruction from image collections"
    - "unsupervised learning of object landmarks through conditional image generation"
    - "3d aware scene manipulation via inverse graphics"
    - "inverserendernet learning single image inverse rendering"

Connected through hyperedge HE-2 (induced by source paper "neural inverse rendering of an indoor scene from a single image"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "deep ordinal regression network for monocular depth estimation"
    - "learning intrinsic image decomposition from watching the world"
    - "inverserendernet learning single image inverse rendering"
    - "inverse transport networks"
    - "materials for masses svbrdf acquisition with a single mobile phone image"

Connected through hyperedge HE-3 (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-4 (induced by source paper "separate in latent space unsupervised single image layer separation"):
  with members:
    - "face image reflection removal"
    - "learning intrinsic image decomposition from watching the world"
    - "unsupervised deep single image intrinsic decomposition using illumination varying image sequences"
    - "single image reflection separation with perceptual losses"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "self supervised intrinsic image decomposition"
    - "a generic deep architecture for single image reflection removal and image smoothing"

Connected through hyperedge HE-5 (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"
    - "shading annotations in the wild"
    - "high resolution image synthesis and semantic manipulation with conditional gans"

Connected through hyperedge HE-6 (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"
    - "shading annotations in the wild"
    - "reflectance adaptive filtering improves intrinsic image estimation"
    - "unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields"
    - "how transferable are features in deep neural networks"
    - "depth transfer depth extraction from video using non parametric sampling"

Connected through hyperedge HE-7 (induced by source paper "separate from observation unsupervised single image layer separation"):
  with members:
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "dualgan unsupervised dual learning for image to image translation"
    - "instance normalization the missing ingredient for fast stylization"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-8 (induced by source paper "intrinseqnet learning to estimate the reflectance from varying illumination"):
  with members:
    - "live intrinsic material estimation"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "learning intrinsic image decomposition from watching the world"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "self supervised intrinsic image decomposition"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "learning data driven reflectance priors for intrinsic image decomposition"

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)