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: "deep sky modeling for single image outdoor lighting estimation"
  Abstract: We propose a data-driven learned sky model, which we use for outdoor lighting estimation from a single image. As no large-scale dataset of images and their corresponding ground truth illumination is readily available, we use complementary datasets to train our approach, combining the vast diversity of illumination conditions of SUN360 with the radiometrically calibrated and physically accurate Laval HDR sky database. Our key contribution is to provide a holistic view of both lighting modeling and estimation, solving both problems end-to-end. From a test image, our method can directly estimate an HDR environment map of the lighting without relying on analytical lighting models. We demonstrate the versatility and expressivity of our learned sky model and show that it can be used to recover plausible illumination, leading to visually pleasant virtual object insertions. To further evaluate our method, we capture a dataset of HDR 360{\deg} panoramas and show through extensive validation that we significantly outperform previous state-of-the-art.

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"
    - "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 "physics based rendering for improving robustness to rain"):
  with members:
    - "nuscenes a multimodal dataset for autonomous driving"
    - "espnet efficient spatial pyramid of dilated convolutions for semantic segmentation"
    - "semantic foggy scene understanding with synthetic data"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "sparse and dense data with cnns depth completion and semantic segmentation"
    - "density aware single image de raining using a multi stream dense network"
    - "icnet for real time semantic segmentation on high resolution images"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "fast spatially varying indoor lighting estimation"):
  with members:
    - "rendering synthetic objects into legacy photographs"
    - "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-4 (induced by source paper "all weather deep outdoor lighting estimation"):
  with members:
    - "learning high dynamic range from outdoor panoramas"
    - "deep outdoor illumination estimation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep reflectance maps"
    - "fast and accurate deep network learning by exponential linear units elus"

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)