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: "joint material and illumination estimation from photo sets in the wild"
  Abstract: Faithful manipulation of shape, material, and illumination in 2D Internet images would greatly benefit from a reliable factorization of appearance into material (i.e., diffuse and specular) and illumination (i.e., environment maps). On the one hand, current methods that produce very high fidelity results, typically require controlled settings, expensive devices, or significant manual effort. To the other hand, methods that are automatic and work on 'in the wild' Internet images, often extract only low-frequency lighting or diffuse materials. In this work, we propose to make use of a set of photographs in order to jointly estimate the non-diffuse materials and sharp lighting in an uncontrolled setting. Our key observation is that seeing multiple instances of the same material under different illumination (i.e., environment), and different materials under the same illumination provide valuable constraints that can be exploited to yield a high-quality solution (i.e., specular materials and environment illumination) for all the observed materials and environments. Similar constraints also arise when observing multiple materials in a single environment, or a single material across multiple environments. The core of this approach is an optimization procedure that uses two neural networks that are trained on synthetic images to predict good gradients in parametric space given observation of reflected light. We evaluate our method on a range of synthetic and real examples to generate high-quality estimates, qualitatively compare our results against state-of-the-art alternatives via a user study, and demonstrate photo-consistent image manipulation that is otherwise very challenging to achieve.

Connected through hyperedge HE-1 (induced by source paper "neural rerendering in the wild"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "video to video synthesis"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "multimodal unsupervised image to image translation"
    - "ignor image guided neural object rendering"
    - "drit diverse image to image translation via disentangled representations"
    - "transformation grounded image generation network for novel 3d view synthesis"
    - "progressive growing of gans for improved quality stability and variation"

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"
    - "inverse transport networks"
    - "materials for masses svbrdf acquisition with a single mobile phone image"

Connected through hyperedge HE-3 (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-4 (induced by source paper "photoshape photorealistic materials for large scale shape collections"):
  with members:
    - "deep inertial poser learning to reconstruct human pose from sparse inertial measurements in real time"
    - "modeling surface appearance from a single photograph using self augmented convolutional neural networks"
    - "material editing using a physically based rendering network"
    - "photographic image synthesis with cascaded refinement networks"
    - "a lightweight approach for on the fly reflectance estimation"
    - "image to image translation with conditional adversarial networks"
    - "playing for data ground truth from computer games"
    - "differential angular imaging for material recognition"

Connected through hyperedge HE-5 (induced by source paper "deepilluminance contextual illuminance estimation via deep neural networks"):
  with members:
    - "approaching the computational color constancy as a classification problem through deep learning"
    - "stacked hourglass networks for human pose estimation"
    - "fast fourier color constancy"
    - "iterative instance segmentation"
    - "convolutional color constancy"
    - "single and multiple illuminant estimation using convolutional neural networks"
    - "learning to compare image patches via convolutional neural networks"
    - "color constancy using cnns"

Connected through hyperedge HE-6 (induced by source paper "deep appearance maps"):
  with members:
    - "live intrinsic material estimation"
    - "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"
    - "material editing using a physically based rendering network"
    - "deep outdoor illumination estimation"
    - "what is around the camera"

Connected through hyperedge HE-7 (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"
    - "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"
    - "delight net decomposing reflectance maps into specular materials and natural illumination"

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.GR(Graphics)