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: "rendering synthetic objects into legacy photographs"
  Abstract: We propose a method to realistically insert synthetic objects into existing photographs without requiring access to the scene or any additional scene measurements. With a single image and a small amount of annotation, our method creates a physical model of the scene that is suitable for realistically rendering synthetic objects with diffuse, specular, and even glowing materials while accounting for lighting interactions between the objects and the scene. We demonstrate in a user study that synthetic images produced by our method are confusable with real scenes, even for people who believe they are good at telling the difference. Further, our study shows that our method is competitive with other insertion methods while requiring less scene information. We also collected new illumination and reflectance datasets; renderings produced by our system compare well to ground truth. Our system has applications in the movie and gaming industry, as well as home decorating and user content creation, among others.

Connected through hyperedge HE-1 (induced by source paper "seeing the world in a bag of chips"):
  with members:
    - "deferred neural rendering image synthesis using neural textures"
    - "neural illumination lighting prediction for indoor environments"
    - "revealing scenes by inverting structure from motion reconstructions"
    - "neural point based graphics"
    - "deep sky modeling for single image outdoor lighting estimation"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "inverse path tracing for joint material and lighting estimation"
    - "deeplight learning illumination for unconstrained mobile mixed reality"

Connected through hyperedge HE-2 (induced by source paper "neural inverse rendering of an indoor scene from a single image"):
  with members:
    - "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"
    - "learning to predict indoor illumination from a single image"

Connected through hyperedge HE-3 (induced by source paper "corners for layout end to end layout recovery from 360 images"):
  with members:
    - "omnidrl robust pedestrian detection using deep reinforcement learning on omnidirectional cameras"
    - "layoutnet reconstructing the 3d room layout from a single rgb image"
    - "dula net a dual projection network for estimating room layouts from a single rgb panorama"
    - "panoroom from the sphere to the 3d layout"
    - "layouts from panoramic images with geometry and deep learning"
    - "physics inspired optimization on semantic transfer features an alternative method for room layout estimation"
    - "deformable convolutional networks"
    - "roomnet end to end room layout estimation"

Connected through hyperedge HE-4 (induced by source paper "fast spatially varying indoor lighting estimation"):
  with members:
    - "deep sky modeling for single image outdoor lighting estimation"
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "taskonomy disentangling task transfer learning"
    - "learning to estimate indoor lighting from 3d objects"
    - "dynamic environment mapping for augmented reality applications on mobile devices"
    - "learning to predict indoor illumination from a single image"
    - "intrinsic3d high quality 3d reconstruction by joint appearance and geometry optimization with spatially varying lighting"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"

Connected through hyperedge HE-5 (induced by source paper "neural illumination lighting prediction for indoor environments"):
  with members:
    - "cgintrinsics better intrinsic image decomposition through physically based rendering"
    - "geometry aware deep network for single image novel view synthesis"
    - "learning to estimate indoor lighting from 3d objects"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "hdr image reconstruction from a single exposure using deep cnns"
    - "learning to predict indoor illumination from a single image"
    - "matterport3d learning from rgb d data in indoor environments"
    - "what is around the camera"

Connected through hyperedge HE-6 (induced by source paper "mimicking the in camera color pipeline for camera aware object compositing"):
  with members:
    - "modelling the scene dependent imaging in cameras with a deep neural network"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "deep image harmonization"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "deep outdoor illumination estimation"
    - "fast fourier color constancy"

Connected through hyperedge HE-7 (induced by source paper "revisiting shadow detection a new benchmark dataset for complex world"):
  with members:
    - "where is my mirror"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "direction aware spatial context features for shadow detection and removal"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "stacked conditional generative adversarial networks for jointly learning shadow detection and shadow removal"
    - "xception deep learning with depthwise separable convolutions"
    - "pyramid scene parsing network"
    - "parsenet looking wider to see better"

Connected through hyperedge HE-8 (induced by source paper "scene text magnifier"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "pixellink detecting scene text via instance segmentation"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "textboxes a single shot oriented scene text detector"
    - "scene text detection and recognition the deep learning era"
    - "ensnet ensconce text in the wild"
    - "scene text eraser"
    - "synthetic data for text localisation in natural images"

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)