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: "what is around the camera"
  Abstract: How much does a single image reveal about the environment it was taken in? In this paper, we investigate how much of that information can be retrieved from a foreground object, combined with the background (i.e. the visible part of the environment). Assuming it is not perfectly diffuse, the foreground object acts as a complexly shaped and far-from-perfect mirror. An additional challenge is that its appearance confounds the light coming from the environment with the unknown materials it is made of. We propose a learning-based approach to predict the environment from multiple reflectance maps that are computed from approximate surface normals. The proposed method allows us to jointly model the statistics of environments and material properties. We train our system from synthesized training data, but demonstrate its applicability to real-world data. Interestingly, our analysis shows that the information obtained from objects made out of multiple materials often is complementary and leads to better performance.

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"
    - "rendering synthetic objects into legacy photographs"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "360 panorama synthesis from a sparse set of images with unknown field of view"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "progressive growing of gans for improved quality stability and variation"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "least squares generative adversarial networks"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-3 (induced by source paper "fast spatially varying indoor lighting estimation"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "360 panorama synthesis from a sparse set of images with unknown fov"):
  with members:
    - "cross view image synthesis using conditional gans"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"
    - "progressive growing of gans for improved quality stability and variation"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "least squares generative adversarial networks"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-5 (induced by source paper "neural illumination lighting prediction for indoor environments"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "deep parametric indoor lighting estimation"):
  with members:
    - "neural illumination lighting prediction for indoor environments"
    - "rendering synthetic objects into legacy photographs"
    - "deeplight learning illumination for unconstrained mobile mixed reality"
    - "learning to predict indoor illumination from a single image"
    - "semantic scene completion from a single depth image"
    - "deep outdoor illumination estimation"

Connected through hyperedge HE-7 (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-8 (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"
    - "joint material and illumination estimation from photo sets in the wild"
    - "material editing using a physically based rendering network"
    - "deep outdoor illumination estimation"

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)