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: "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
  Abstract: We present a convolutional neural network (CNN) based solution for modeling physically plausible spatially varying surface reflectance functions (SVBRDF) from a single photograph of a planar material sample under unknown natural illumination. Gathering a sufficiently large set of labeled training pairs consisting of photographs of SVBRDF samples and corresponding reflectance parameters, is a difficult and arduous process. To reduce the amount of required labeled training data, we propose to leverage the appearance information embedded in unlabeled images of spatially varying materials to self-augment the training process. Starting from an initial approximative network obtained from a small set of labeled training pairs, we estimate provisional model parameters for each unlabeled training exemplar. Given this provisional reflectance estimate, we then synthesize a novel temporary labeled training pair by rendering the exact corresponding image under a new lighting condition. After refining the network using these additional training samples, we re-estimate the provisional model parameters for the unlabeled data and repeat the self-augmentation process until convergence. We demonstrate the efficacy of the proposed network structure on spatially varying wood, metals, and plastics, as well as thoroughly validate the effectiveness of the self-augmentation training process.

Connected through hyperedge HE-1 (induced by source paper "deltra deep light transport for projector camera systems"):
  with members:
    - "pro cam ssfm projector camera system for structure and spectral reflectance from motion"
    - "compennet end to end full projector compensation"
    - "kornia an open source differentiable computer vision library for pytorch"
    - "self calibrating deep photometric stereo networks"
    - "end to end projector photometric compensation"
    - "neural inverse rendering for general reflectance photometric stereo"
    - "a single shot per pose camera projector calibration system for imperfect planar targets"
    - "inverserendernet learning single image inverse rendering"

Connected through hyperedge HE-2 (induced by source paper "a method for estimating reflectance map and material using deep learning with synthetic dataset"):
  with members:
    - "material editing using a physically based rendering network"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "depth map prediction from a single image using a multi scale deep network"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"
    - "deep convolutional neural fields for depth estimation from a single image"

Connected through hyperedge HE-3 (induced by source paper "neural voxel renderer learning an accurate and controllable rendering tool"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "neural rendering and reenactment of human actor videos"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"

Connected through hyperedge HE-4 (induced by source paper "physics based neural networks for shape from polarization"):
  with members:
    - "neural lander stable drone landing control using learned dynamics"
    - "reblur2deblur deblurring videos via self supervised learning"
    - "inverse transport networks"
    - "learning a discriminative prior for blind image deblurring"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "single image svbrdf capture with a rendering aware deep network"
    - "flash photography for data driven hidden scene recovery"

Connected through hyperedge HE-5 (induced by source paper "photoshape photorealistic materials for large scale shape collections"):
  with members:
    - "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"
    - "joint material and illumination estimation from photo sets in the wild"
    - "material editing using a physically based rendering network"
    - "photographic image synthesis with cascaded refinement networks"
    - "a lightweight approach for on the fly reflectance estimation"
    - "image to image translation with conditional adversarial networks"
    - "playing for data ground truth from computer games"
    - "differential angular imaging for material recognition"

Connected through hyperedge HE-6 (induced by source paper "photorealistic material editing through direct image manipulation"):
  with members:
    - "deepred deep image prior powered by red"
    - "analyzing inverse problems with invertible neural networks"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "single image svbrdf capture with a rendering aware deep network"
    - "gaussian material synthesis"
    - "dynamic routing between capsules"
    - "neural optimizer search with reinforcement learning"
    - "an intuitive control space for material appearance"

Connected through hyperedge HE-7 (induced by source paper "relighting humans occlusion aware inverse rendering for full body human images"):
  with members:
    - "detailed accurate human shape estimation from clothed 3d scan sequences"
    - "learning to predict indoor illumination from a single image"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"
    - "3d shape reconstruction from sketches via multi view convolutional networks"
    - "cnn based learning using reflection and retinex models for intrinsic image decomposition"
    - "sfsnet learning shape reflectance and illuminance of faces in the wild"
    - "deep outdoor illumination estimation"

Connected through hyperedge HE-8 (induced by source paper "flexible svbrdf capture with a multi image deep network"):
  with members:
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "deep sets"
    - "single image svbrdf capture with a rendering aware deep network"
    - "ps fcn a flexible learning framework for photometric stereo"
    - "material editing using a physically based rendering network"
    - "silnet single and multi view reconstruction by learning from silhouettes"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"

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.GR(Graphics)