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: "a system for acquiring processing and rendering panoramic light field stills for virtual reality"
  Abstract: We present a system for acquiring, processing, and rendering panoramic light field still photography for display in Virtual Reality (VR). We acquire spherical light field datasets with two novel light field camera rigs designed for portable and efficient light field acquisition. We introduce a novel real-time light field reconstruction algorithm that uses a per-view geometry and a disk-based blending field. We also demonstrate how to use a light field prefiltering operation to project from a high-quality offline reconstruction model into our real-time model while suppressing artifacts. We introduce a practical approach for compressing light fields by modifying the VP9 video codec to provide high quality compression with real-time, random access decompression.#R##N##R##N#We combine these components into a complete light field system offering convenient acquisition, compact file size, and high-quality rendering while generating stereo views at 90Hz on commodity VR hardware. Using our system, we built a freely available light field experience application called Welcome to Light Fields featuring a library of panoramic light field stills for consumer VR which has been downloaded over 15,000 times.

Connected through hyperedge HE-1 (induced by source paper "neural volumes learning dynamic renderable volumes from images"):
  with members:
    - "raynet learning volumetric 3d reconstruction with ray potentials"
    - "video to video synthesis"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "lookingood enhancing performance capture with real time neural re rendering"
    - "multi view consistency as supervisory signal for learning shape and pose prediction"
    - "deep appearance models for face rendering"
    - "stereo magnification learning view synthesis using multiplane images"
    - "rendernet a deep convolutional network for differentiable rendering from 3d shapes"

Connected through hyperedge HE-2 (induced by source paper "learning occlusion aware view synthesis for light fields"):
  with members:
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "context aware synthesis for video frame interpolation"
    - "stereo magnification learning view synthesis using multiplane images"
    - "pyramid stereo matching network"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "end to end learning of geometry and context for deep stereo regression"
    - "residual conv deconv grid network for semantic segmentation"
    - "frame interpolation with multi scale deep loss functions and generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "local light field fusion practical view synthesis with prescriptive sampling guidelines"):
  with members:
    - "deepmvs learning multi view stereopsis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "photographic image synthesis with cascaded refinement networks"
    - "semantic scene completion from a single depth image"
    - "learning based view synthesis for light field cameras"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-4 (induced by source paper "lapepi net a laplacian pyramid epi structure for learning based dense light field reconstruction"):
  with members:
    - "crossnet an end to end reference based super resolution network using cross scale warping"
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning based view synthesis for light field cameras"
    - "light field reconstruction using shearlet transform"
    - "deepstereo learning to predict new views from the world s imagery"
    - "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.GR(Graphics)