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: "end to end learning of geometry and context for deep stereo regression"
  Abstract: We propose a novel deep learning architecture for regressing disparity from a rectified pair of stereo images. We leverage knowledge of the problem's geometry to form a cost volume using deep feature representations. We learn to incorporate contextual information using 3-D convolutions over this volume. Disparity values are regressed from the cost volume using a proposed differentiable soft argmin operation, which allows us to train our method end-to-end to sub-pixel accuracy without any additional post-processing or regularization. We evaluate our method on the Scene Flow and KITTI datasets and on KITTI we set a new state-of-the-art benchmark, while being significantly faster than competing approaches.

Connected through hyperedge HE-1 (induced by source paper "learning occlusion aware view synthesis for light fields"):
  with members:
    - "a system for acquiring processing and rendering panoramic light field stills for virtual reality"
    - "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"
    - "residual conv deconv grid network for semantic segmentation"
    - "frame interpolation with multi scale deep loss functions and generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "navigationnet a large scale interactive indoor navigation dataset"):
  with members:
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "dual path networks"
    - "matterport3d learning from rgb d data in indoor environments"
    - "emergence of locomotion behaviours in rich environments"
    - "end to end learning of semantic grasping"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-3 (induced by source paper "deepv2d video to depth with differentiable structure from motion"):
  with members:
    - "deeptam deep tracking and mapping"
    - "deep auxiliary learning for visual localization and odometry"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "ls net learning to solve nonlinear least squares for monocular stereo"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "high quality monocular depth estimation via transfer learning"

Connected through hyperedge HE-4 (induced by source paper "fast view synthesis with deep stereo vision"):
  with members:
    - "novel view synthesis for large scale scene using adversarial loss"
    - "geometry aware deep network for single image novel view synthesis"
    - "self supervised learning for stereo matching with self improving ability"
    - "unsupervised learning of depth and ego motion from video"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "unsupervised monocular depth estimation with left right consistency"
    - "deeper depth prediction with fully convolutional residual networks"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"

Connected through hyperedge HE-5 (induced by source paper "left right comparative recurrent model for stereo matching"):
  with members:
    - "continuous 3d label stereo matching using local expansion moves"
    - "self supervised learning for stereo matching with self improving ability"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "semi supervised deep learning for monocular depth map prediction"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "improved stereo matching with constant highway networks and reflective confidence learning"

Connected through hyperedge HE-6 (induced by source paper "unsupervised learning of depth and ego motion from video"):
  with members:
    - "multi view supervision for single view reconstruction via differentiable ray consistency"
    - "demon depth and motion network for learning monocular stereo"
    - "sfm net learning of structure and motion from video"
    - "semi supervised deep learning for monocular depth map prediction"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "deep3d fully automatic 2d to 3d video conversion with deep convolutional neural networks"
    - "unsupervised monocular depth estimation with left right consistency"

Connected through hyperedge HE-7 (induced by source paper "cascade residual learning a two stage convolutional neural network for stereo matching"):
  with members:
    - "semi supervised deep learning for monocular depth map prediction"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "detect replace refine deep structured prediction for pixel wise labeling"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "spatial transformer networks"

Connected through hyperedge HE-8 (induced by source paper "widening siamese architectures for stereo matching"):
  with members:
    - "look wider to match image patches with convolutional neural networks"
    - "cascade residual learning a two stage convolutional neural network for stereo matching"
    - "improved stereo matching with constant highway networks and reflective confidence learning"
    - "end to end training of hybrid cnn crf models for stereo"
    - "stereo matching by training a convolutional neural network to compare image patches"
    - "flownet learning optical flow with convolutional networks"
    - "adam a method for stochastic optimization"
    - "imagenet large scale visual recognition challenge"

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)