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: "self supervised intrinsic image decomposition"
  Abstract: Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervised learning approaches, in this paper we propose learning intrinsic image decomposition by explaining the input image. Our model, the Rendered Intrinsics Network (RIN), joins together an image decomposition pipeline, which predicts reflectance, shape, and lighting conditions given a single image, with a recombination function, a learned shading model used to recompose the original input based off of intrinsic image predictions. Our network can then use unsupervised reconstruction error as an additional signal to improve its intermediate representations. This allows large-scale unlabeled data to be useful during training, and also enables transferring learned knowledge to images of unseen object categories, lighting conditions, and shapes. Extensive experiments demonstrate that our method performs well on both intrinsic image decomposition and knowledge transfer.

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 "deep audio prior"):
  with members:
    - "photometric mesh optimization for video aligned 3d object reconstruction"
    - "audio denoising with deep network priors"
    - "double dip unsupervised image decomposition via coupled deep image priors"
    - "self supervised generation of spatial audio for 360 video"
    - "single image reflection separation with perceptual losses"
    - "does k matter k nn hubness analysis for kernel additive modelling vocal separation"
    - "improving dnn based music source separation using phase features"
    - "wave u net a multi scale neural network for end to end audio source separation"

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"
    - "cnn based learning using reflection and retinex models for 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"
    - "a generic deep architecture for single image reflection removal and image smoothing"
    - "dualgan unsupervised dual learning for image to image translation"

Connected through hyperedge HE-5 (induced by source paper "three d safari learning to estimate zebra pose shape and texture from images in the wild"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "learning to estimate 3d human pose and shape from a single color image"
    - "video based reconstruction of 3d people models"
    - "neural body fitting unifying deep learning and model based human pose and shape estimation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "creatures great and smal recovering the shape and motion of animals from video"
    - "3d pose regression using convolutional neural networks"
    - "end to end recovery of human shape and pose"

Connected through hyperedge HE-6 (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"
    - "shading annotations in the wild"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-7 (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"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (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"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "shading annotations in the wild"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"

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)