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: "delight net decomposing reflectance maps into specular materials and natural illumination"
  Abstract: In this paper we are extracting surface reflectance and natural environmental illumination from a reflectance map, i.e. from a single 2D image of a sphere of one material under one illumination. This is a notoriously difficult problem, yet key to various re-rendering applications. With the recent advances in estimating reflectance maps from 2D images their further decomposition has become increasingly relevant. #R##N#To this end, we propose a Convolutional Neural Network (CNN) architecture to reconstruct both material parameters (i.e. Phong) as well as illumination (i.e. high-resolution spherical illumination maps), that is solely trained on synthetic data. We demonstrate that decomposition of synthetic as well as real photographs of reflectance maps, both in High Dynamic Range (HDR), and, for the first time, on Low Dynamic Range (LDR) as well. Results are compared to previous approaches quantitatively as well as qualitatively in terms of re-renderings where illumination, material, view or shape are changed.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "deeplight learning illumination for unconstrained mobile mixed reality"):
  with members:
    - "learning to estimate indoor lighting from 3d objects"
    - "generative image inpainting with contextual attention"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "learning high dynamic range from outdoor panoramas"
    - "learning to predict indoor illumination from a single image"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "neural face editing with intrinsic image disentangling"

Connected through hyperedge HE-3 (induced by source paper "specular to diffuse translation for multi view reconstruction"):
  with members:
    - "lime live intrinsic material estimation"
    - "deep adversarial attention alignment for unsupervised domain adaptation the benefit of target expectation maximization"
    - "perceptual adversarial networks for image to image transformation"
    - "superpoint self supervised interest point detection and description"
    - "learning efficient point cloud generation for dense 3d object reconstruction"
    - "cycada cycle consistent adversarial domain adaptation"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "materials for masses svbrdf acquisition with a single mobile phone image"):
  with members:
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "material editing using a physically based rendering network"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "a lightweight approach for on the fly reflectance estimation"
    - "3d r2n2 a unified approach for single and multi view 3d object reconstruction"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "shapenet an information rich 3d model repository"
    - "deep reflectance maps"

Connected through hyperedge HE-5 (induced by source paper "learning to estimate indoor lighting from 3d objects"):
  with members:
    - "learning to predict indoor illumination from a single image"
    - "deep 6 dof tracking"
    - "material editing using a physically based rendering network"
    - "matterport3d learning from rgb d data in indoor environments"
    - "learning a predictable and generative vector representation for objects"
    - "deep outdoor illumination estimation"
    - "what is around the camera"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "robust point light source estimation using differentiable rendering"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "learning to predict indoor illumination from a single image"
    - "mofa model based deep convolutional face autoencoder for unsupervised monocular reconstruction"
    - "joint material and illumination estimation from photo sets in the wild"
    - "self supervised intrinsic image decomposition"
    - "sfsnet learning shape reflectance and illuminance of faces in the wild"
    - "self supervised multi level face model learning for monocular reconstruction at over 250 hz"
    - "deep outdoor illumination estimation"

Connected through hyperedge HE-7 (induced by source paper "material editing using a physically based rendering network"):
  with members:
    - "perceptual losses for real time style transfer and super resolution"
    - "learning non lambertian object intrinsics across shapenet categories"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "shapenet an information rich 3d model repository"
    - "deep convolutional inverse graphics network"
    - "deep reflectance maps"
    - "direct intrinsics learning albedo shading decomposition by convolutional regression"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "deep outdoor illumination estimation"):
  with members:
    - "find your way by observing the sun and other semantic cues"
    - "marr revisited 2d 3d alignment via surface normal prediction"
    - "learning data driven reflectance priors for intrinsic image decomposition"
    - "deep convolutional inverse graphics network"
    - "deep reflectance maps"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

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)