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: "neural illumination lighting prediction for indoor environments"
  Abstract: This paper addresses the task of estimating the light arriving from all directions to a 3D point observed at a selected pixel in an RGB image. This task is challenging because it requires predicting a mapping from a partial scene observation by a camera to a complete illumination map for a selected position, which depends on the 3D location of the selection, the distribution of unobserved light sources, the occlusions caused by scene geometry, etc. Previous methods attempt to learn this complex mapping directly using a single black-box neural network, which often fails to estimate high-frequency lighting details for scenes with complicated 3D geometry. Instead, we propose "Neural Illumination" a new approach that decomposes illumination prediction into several simpler differentiable sub-tasks: 1) geometry estimation, 2) scene completion, and 3) LDR-to-HDR estimation. The advantage of this approach is that the sub-tasks are relatively easy to learn and can be trained with direct supervision, while the whole pipeline is fully differentiable and can be fine-tuned with end-to-end supervision. Experiments show that our approach performs significantly better quantitatively and qualitatively than prior work.

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"
    - "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"
    - "deeplight learning illumination for unconstrained mobile mixed reality"

Connected through hyperedge HE-2 (induced by source paper "lighthouse predicting lighting volumes for spatially coherent illumination"):
  with members:
    - "fast spatially varying indoor lighting estimation"
    - "neural inverse rendering of an indoor scene from a single image"
    - "inverse rendering for complex indoor scenes shape spatially varying lighting and svbrdf from a single image"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "neural volumes learning dynamic renderable volumes from images"
    - "deeplight learning illumination for unconstrained mobile mixed reality"
    - "semantic image synthesis with spatially adaptive normalization"
    - "deep parametric indoor lighting estimation"

Connected through hyperedge HE-3 (induced by source paper "blending diverse physical priors with neural networks"):
  with members:
    - "combining physical simulators and object based networks for control"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "block coordinate regularization by denoising"
    - "physics based neural networks for shape from polarization"
    - "heavy rain image restoration integrating physics model and conditional adversarial learning"
    - "neural lander stable drone landing control using learned dynamics"
    - "deep learning approach for fourier ptychography microscopy"
    - "learning to separate multiple illuminants in a single image"

Connected through hyperedge HE-4 (induced by source paper "deep parametric indoor lighting estimation"):
  with members:
    - "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"
    - "what is around the camera"

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)