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: "materials for masses svbrdf acquisition with a single mobile phone image"
  Abstract: We propose a material acquisition approach to recover the spatially-varying BRDF and normal map of a near-planar surface from a single image captured by a handheld mobile phone camera. Our method images the surface under arbitrary environment lighting with the flash turned on, thereby avoiding shadows while simultaneously capturing high-frequency specular highlights. We train a CNN to regress an SVBRDF and surface normals from this image. Our network is trained using a large-scale SVBRDF dataset and designed to incorporate physical insights for material estimation, including an in-network rendering layer to model appearance and a material classifier to provide additional supervision during training. We refine the results from the network using a dense CRF module whose terms are designed specifically for our task. The framework is trained end-to-end and produces high quality results for a variety of materials. We provide extensive ablation studies to evaluate our network on both synthetic and real data, while demonstrating significant improvements in comparisons with prior works.

Connected through hyperedge HE-1 (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-2 (induced by source paper "a neural rendering framework for free viewpoint relighting"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "visual representation learning with 3d view contrastive inverse graphics networks"
    - "transformable bottleneck networks"
    - "differentiable surface splatting for point based geometry processing"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "monet unsupervised scene decomposition and representation"
    - "scene representation networks continuous 3d structure aware neural scene representations"
    - "neural point based graphics"

Connected through hyperedge HE-3 (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"
    - "learning to predict indoor illumination from a single image"

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"
    - "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"
    - "physics based generative adversarial models for image restoration and beyond"

Connected through hyperedge HE-5 (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"
    - "single image svbrdf capture with a rendering aware deep network"
    - "gaussian material synthesis"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "dynamic routing between capsules"
    - "neural optimizer search with reinforcement learning"
    - "an intuitive control space for material appearance"

Connected through hyperedge HE-6 (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"
    - "deep sets"
    - "single image svbrdf capture with a rendering aware deep network"
    - "ps fcn a flexible learning framework for photometric stereo"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "single image brdf parameter estimation with a conditional adversarial network"):
  with members:
    - "single image svbrdf capture with a rendering aware deep network"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "image to image translation with conditional adversarial networks"
    - "enhancenet single image super resolution through automated texture synthesis"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "a bayesian inference framework for procedural material parameter estimation"):
  with members:
    - "single image svbrdf capture with a rendering aware deep network"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "a neural algorithm of artistic style"
    - "very deep convolutional networks for large scale image recognition"

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)