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: "material editing using a physically based rendering network"
  Abstract: The ability to edit materials of objects in images is desirable by many content creators. However, this is an extremely challenging task as it requires to disentangle intrinsic physical properties of an image. We propose an end-to-end network architecture that replicates the forward image formation process to accomplish this task. Specifically, given a single image, the network first predicts intrinsic properties, i.e. shape, illumination, and material, which are then provided to a rendering layer. This layer performs in-network image synthesis, thereby enabling the network to understand the physics behind the image formation process. The proposed rendering layer is fully differentiable, supports both diffuse and specular materials, and thus can be applicable in a variety of problem settings. We demonstrate a rich set of visually plausible material editing examples and provide an extensive comparative study.

Connected through hyperedge HE-1 (induced by source paper "a method for estimating reflectance map and material using deep learning with synthetic dataset"):
  with members:
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "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-2 (induced by source paper "differentiable volumetric rendering learning implicit 3d representations without 3d supervision"):
  with members:
    - "pix2vox context aware 3d reconstruction from single and multi view images"
    - "learning shape templates with structured implicit functions"
    - "controlling neural level sets"
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "learning to predict 3d objects with an interpolation based differentiable renderer"
    - "texture fields learning texture representations in function space"

Connected through hyperedge HE-3 (induced by source paper "differentiable visual computing"):
  with members:
    - "on the convergence of adam and beyond"
    - "elimination of all bad local minima in deep learning"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stochastic gradient descent with biased but consistent gradient estimators"
    - "line drawings from 3d models"
    - "tiramisu a polyhedral compiler for expressing fast and portable code"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"
    - "tangent automatic differentiation using source code transformation in python"

Connected through hyperedge HE-4 (induced by source paper "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"):
  with members:
    - "learning category specific mesh reconstruction from image collections"
    - "geometry aware network for non rigid shape prediction from a single view"
    - "hairnet single view hair reconstruction using convolutional neural networks"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "siclope silhouette based clothed people"
    - "single image svbrdf capture with a rendering aware deep network"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

Connected through hyperedge HE-5 (induced by source paper "soft rasterizer a differentiable renderer for image based 3d reasoning"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "learning category specific mesh reconstruction from image collections"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "unsupervised training for 3d morphable model regression"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "single image svbrdf capture with a rendering aware deep network"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "nonlinear 3d face morphable model"

Connected through hyperedge HE-6 (induced by source paper "differentiable surface splatting for point based geometry processing"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "render4completion synthesizing multi view depth maps for 3d shape completion"
    - "pointcleannet learning to denoise and remove outliers from dense point clouds"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "inverse path tracing for joint material and lighting estimation"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "3d aware scene manipulation via inverse graphics"
    - "unsupervised learning of shape and pose with differentiable point clouds"

Connected through hyperedge HE-7 (induced by source paper "learning to infer implicit surfaces without 3d supervision"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "inverse path tracing for joint material and lighting estimation"
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "deep level sets implicit surface representations for 3d shape inference"
    - "learning implicit fields for generative shape modeling"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "atlasnet a papier m ach e approach to learning 3d surface generation"
    - "unsupervised training for 3d morphable model regression"

Connected through hyperedge HE-8 (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"

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)