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: "local light field fusion practical view synthesis with prescriptive sampling guidelines"
  Abstract: We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration. Previous approaches either require intractably dense view sampling or provide little to no guidance for how users should sample views of a scene to reliably render high-quality novel views. Instead, we propose an algorithm for view synthesis from an irregular grid of sampled views that first expands each sampled view into a local light field via a multiplane image (MPI) scene representation, then renders novel views by blending adjacent local light fields. We extend traditional plenoptic sampling theory to derive a bound that specifies precisely how densely users should sample views of a given scene when using our algorithm. In practice, we apply this bound to capture and render views of real world scenes that achieve the perceptual quality of Nyquist rate view sampling while using up to 4000x fewer views. We demonstrate our approach's practicality with an augmented reality smartphone app that guides users to capture input images of a scene and viewers that enable realtime virtual exploration on desktop and mobile platforms.

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"
    - "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:
    - "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"
    - "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 "nerf representing scenes as neural radiance fields for view synthesis"):
  with members:
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "deepview view synthesis with learned gradient descent"
    - "neural volumes learning dynamic renderable volumes from images"
    - "differentiable volumetric rendering learning implicit 3d representations without 3d supervision"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "unsupervised training for 3d morphable model regression"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-4 (induced by source paper "learning light field synthesis with multi plane images scene encoding as a recurrent segmentation task"):
  with members:
    - "deepview view synthesis with learned gradient descent"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "3d ken burns effect from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "deferred neural rendering image synthesis using neural textures"
    - "transformable bottleneck networks"
    - "neural point based graphics"
    - "learning the depths of moving people by watching frozen people"
    - "depth from videos in the wild unsupervised monocular depth learning from unknown cameras"
    - "neural rerendering in the wild"
    - "edgeconnect generative image inpainting with adversarial edge learning"

Connected through hyperedge HE-6 (induced by source paper "monocular neural image based rendering with continuous view control"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "transformable bottleneck networks"
    - "deepview view synthesis with learned gradient descent"
    - "soft rasterizer differentiable rendering for unsupervised single view mesh reconstruction"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "deepvoxels learning persistent 3d feature embeddings"
    - "visual object networks image generation with disentangled 3d representation"
    - "unsupervised geometry aware representation for 3d human pose estimation"

Connected through hyperedge HE-7 (induced by source paper "5d light field synthesis from a monocular video"):
  with members:
    - "synthesizing a 4d spatio angular consistent light field from a single image"
    - "pushing the boundaries of view extrapolation with multiplane images"
    - "df net unsupervised joint learning of depth and flow using cross task consistency"
    - "deepmvs learning multi view stereopsis"
    - "learning blind video temporal consistency"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "epinet a fully convolutional neural network using epipolar geometry for depth from light field images"
    - "stereo magnification learning view synthesis using multiplane images"

Connected through hyperedge HE-8 (induced by source paper "neural view interpolation for sparse light field video"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "deepview view synthesis with learned gradient descent"
    - "neural volumes learning dynamic renderable volumes from images"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "stereo magnification learning view synthesis using multiplane images"
    - "learning to synthesize a 4d rgbd light field from a single image"
    - "sampling generative networks"
    - "neural audio synthesis of musical notes with wavenet autoencoders"

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)