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: "deeplight learning illumination for unconstrained mobile mixed reality"
  Abstract: We present a learning-based method to infer plausible high dynamic range (HDR), omnidirectional illumination given an unconstrained, low dynamic range (LDR) image from a mobile phone camera with a limited field of view (FOV). For training data, we collect videos of various reflective spheres placed within the camera's FOV, leaving most of the background unoccluded, leveraging that materials with diverse reflectance functions reveal different lighting cues in a single exposure. We train a deep neural network to regress from the LDR background image to HDR lighting by matching the LDR ground truth sphere images to those rendered with the predicted illumination using image-based relighting, which is differentiable. Our inference runs at interactive frame rates on a mobile device, enabling realistic rendering of virtual objects into real scenes for mobile mixed reality. Training on automatically exposed and white-balanced videos, we improve the realism of rendered objects compared to the state-of-the art methods for both indoor and outdoor scenes.

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 "lighthouse predicting lighting volumes for spatially coherent illumination"):
  with members:
    - "neural illumination lighting prediction for indoor environments"
    - "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"
    - "semantic image synthesis with spatially adaptive normalization"
    - "deep parametric indoor lighting estimation"

Connected through hyperedge HE-3 (induced by source paper "scene aware audio rendering via deep acoustic analysis"):
  with members:
    - "scenecode monocular dense semantic reconstruction using learned encoded scene representations"
    - "data augmentation and deep convolutional neural networks for blind room acoustic parameter estimation"
    - "impulse response data augmentation and deep neural networks for blind room acoustic parameter estimation"
    - "regression and classification for direction of arrival estimation with convolutional recurrent neural networks"
    - "low frequency compensated synthetic impulse responses for improved far field speech recognition"
    - "improving reverberant speech training using diffuse acoustic simulation"
    - "scene aware audio for 360 videos"
    - "codeslam learning a compact optimisable representation for dense visual slam"

Connected through hyperedge HE-4 (induced by source paper "deep parametric indoor lighting estimation"):
  with members:
    - "neural illumination lighting prediction for indoor environments"
    - "rendering synthetic objects into legacy photographs"
    - "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)