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: "deepview view synthesis with learned gradient descent"
  Abstract: We present a novel approach to view synthesis using multiplane images (MPIs). Building on recent advances in learned gradient descent, our algorithm generates an MPI from a set of sparse camera viewpoints. The resulting method incorporates occlusion reasoning, improving performance on challenging scene features such as object boundaries, lighting reflections, thin structures, and scenes with high depth complexity. We show that our method achieves high-quality, state-of-the-art results on two datasets: the Kalantari light field dataset, and a new camera array dataset, Spaces, which we make publicly available.

Connected through hyperedge HE-1 (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"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "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-2 (induced by source paper "learning light field synthesis with multi plane images scene encoding as a recurrent segmentation task"):
  with members:
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "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-3 (induced by source paper "drst deep residual shearlet transform for densely sampled light field reconstruction"):
  with members:
    - "high speed video from asynchronous camera array"
    - "video frame interpolation via adaptive separable convolution"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (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"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "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-5 (induced by source paper "synsin end to end view synthesis from a single image"):
  with members:
    - "hologan unsupervised learning of 3d representations from natural images"
    - "differentiable surface splatting for point based geometry processing"
    - "neural point based graphics"
    - "the replica dataset a digital replica of indoor spaces"
    - "linearized multi sampling for differentiable image transformation"
    - "neural volumes learning dynamic renderable volumes from images"
    - "semantic image synthesis with spatially adaptive normalization"
    - "mesh r cnn"

Connected through hyperedge HE-6 (induced by source paper "neural view interpolation for sparse light field video"):
  with members:
    - "pix2vex image to geometry reconstruction using a smooth differentiable renderer"
    - "local light field fusion practical view synthesis with prescriptive sampling guidelines"
    - "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)