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: "inverse transport networks"
  Abstract: We introduce inverse transport networks as a learning architecture for inverse rendering problems where, given input image measurements, we seek to infer physical scene parameters such as shape, material, and illumination. During training, these networks are evaluated not only in terms of how close they can predict groundtruth parameters, but also in terms of whether the parameters they produce can be used, together with physically-accurate graphics renderers, to reproduce the input image measurements. To en- able training of inverse transport networks using stochastic gradient descent, we additionally create a general-purpose, physically-accurate differentiable renderer, which can be used to estimate derivatives of images with respect to arbitrary physical scene parameters. Our experiments demonstrate that inverse transport networks can be trained efficiently using differentiable rendering, and that they generalize to scenes with completely unseen geometry and illumination better than networks trained without appearance- matching regularization.

Connected through hyperedge HE-1 (induced by source paper "pix2vex image to geometry reconstruction using a smooth differentiable renderer"):
  with members:
    - "soft rasterizer a differentiable renderer for image based 3d reasoning"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "live intrinsic material estimation"
    - "learning category specific mesh reconstruction from image collections"
    - "first experiments with neural translation of informal to formal mathematics"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"
    - "beyond pixel norm balls parametric adversaries using an analytically differentiable renderer"

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"
    - "materials for masses svbrdf acquisition with a single mobile phone image"
    - "learning to predict indoor illumination from a single image"

Connected through hyperedge HE-3 (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"
    - "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"
    - "physics based generative adversarial models for image restoration and beyond"

Connected through hyperedge HE-4 (induced by source paper "algonet c infty smooth algorithmic neural networks"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "learning to generate and reconstruct 3d meshes with only 2d supervision"
    - "explaining explanations an overview of interpretability of machine learning"
    - "approximate program smoothing using mean variance statistics with application to procedural shader bandlimiting"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "inverse path tracing for joint material and lighting estimation"):
  with members:
    - "single image svbrdf capture with a rendering aware deep network"
    - "intrinsic3d high quality 3d reconstruction by joint appearance and geometry optimization with spatially varying lighting"
    - "neural 3d mesh renderer"
    - "bundlefusion real time globally consistent 3d reconstruction using on the fly surface re integration"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "inverse rendering for complex indoor scenes shape spatially varying lighting and svbrdf from a single image"):
  with members:
    - "shapenet an information rich 3d model repository"
    - "material recognition in the wild with the materials in context database"

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)