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: "learning to estimate indoor lighting from 3d objects"
  Abstract: In this work, we propose a step towards a more accurate prediction of the environment light given a single picture of a known object. To achieve this, we developed a deep learning method that is able to encode the latent space of indoor lighting using few parameters and that is trained on a database of environment maps. This latent space is then used to generate predictions of the light that are both more realistic and accurate than previous methods. To achieve this, our first contribution is a deep autoencoder which is capable of learning the feature space that compactly models lighting. Our second contribution is a convolutional neural network that predicts the light from a single image of a known object. To train these networks, our third contribution is a novel dataset that contains 21,000 HDR indoor environment maps. The results indicate that the predictor can generate plausible lighting estimations even from diffuse objects.

Connected through hyperedge HE-1 (induced by source paper "deeplight learning illumination for unconstrained mobile mixed reality"):
  with members:
    - "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"
    - "joint material and illumination estimation from photo sets in the wild"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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-4 (induced by source paper "single image portrait relighting"):
  with members:
    - "relighting humans occlusion aware inverse rendering for full body human images"
    - "synthetic depth of field with a single camera mobile phone"
    - "group normalization"
    - "learning to predict indoor illumination from a single image"
    - "deep bilateral learning for real time image enhancement"
    - "sfsnet learning shape reflectance and illuminance of faces in the wild"
    - "deep outdoor illumination estimation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "self calibrating deep photometric stereo networks"):
  with members:
    - "neural inverse rendering for general reflectance photometric stereo"
    - "cnn ps cnn based photometric stereo for general non convex surfaces"
    - "ps fcn a flexible learning framework for photometric stereo"
    - "learning to predict indoor illumination from a single image"
    - "label denoising adversarial network ldan for inverse lighting of face images"
    - "silnet single and multi view reconstruction by learning from silhouettes"
    - "deep outdoor illumination estimation"
    - "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)