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: "live intrinsic material estimation"
  Abstract: We present the first end-to-end approach for real-time material estimation for general object shapes that only requires a single color image as input. In addition to Lambertian surface properties, our approach fully automatically computes the specular albedo, material shininess, and a foreground segmentation. We tackle this challenging and ill-posed inverse rendering problem using recent advances in image-to-image translation techniques based on deep convolutional encoder-decoder architectures. The underlying core representations of our approach are specular shading, diffuse shading and mirror images, which allow to learn the effective and accurate separation of diffuse and specular albedo. In addition, we propose a novel highly efficient perceptual rendering loss that mimics real-world image formation and obtains intermediate results even during run time. The estimation of material parameters at real-time frame rates enables exciting mixed-reality applications, such as seamless illumination-consistent integration of virtual objects into real-world scenes, and virtual material cloning. We demonstrate our approach in a live setup, compare it to the state of the art, and motivate its effectiveness through quantitative and qualitative evaluation.

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"
    - "learning category specific mesh reconstruction from image collections"
    - "inverse transport networks"
    - "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 "intrinseqnet learning to estimate the reflectance from varying illumination"):
  with members:
    - "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"
    - "self supervised intrinsic image decomposition"
    - "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-3 (induced by source paper "deep multi class adversarial specularity removal"):
  with members:
    - "generative collaborative networks for single image super resolution"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "lime live intrinsic material estimation"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "nips 2016 tutorial generative adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "deep appearance maps"):
  with members:
    - "single image svbrdf capture with a rendering aware deep network"
    - "lime live intrinsic material estimation"
    - "deep shading convolutional neural networks for screen space shading"
    - "enhanced deep residual networks for single image super resolution"
    - "joint material and illumination estimation from photo sets in the wild"
    - "material editing using a physically based rendering network"
    - "deep outdoor illumination estimation"
    - "what is around the camera"

Connected through hyperedge HE-5 (induced by source paper "joint material and illumination estimation from photo sets in the wild"):
  with members:
    - "material editing using a physically based rendering network"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "novel views of objects from a single image"
    - "natural illumination from multiple materials using deep learning"
    - "render for cnn viewpoint estimation in images using cnns trained with rendered 3d model views"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "designing deep networks for surface normal estimation"
    - "predicting depth surface normals and semantic labels with a common multi scale convolutional architecture"

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)