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: "unsupervised learning of depth and motion"
  Abstract: We present a model for the joint estimation of disparity and motion. The model is based on learning about the interrelations between images from multiple cameras, multiple frames in a video, or the combination of both. We show that learning depth and motion cues, as well as their combinations, from data is possible within a single type of architecture and a single type of learning algorithm, by using biologically inspired "complex cell" like units, which encode correlations between the pixels across image pairs. Our experimental results show that the learning of depth and motion makes it possible to achieve state-of-the-art performance in 3-D activity analysis, and to outperform existing hand-engineered 3-D motion features by a very large margin.

Connected through hyperedge HE-1 (induced by source paper "ad vo scale resilient visual odometry using attentive disparity map"):
  with members:
    - "unsupervised learning of depth and ego motion from video"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "unsupervised monocular depth estimation with left right consistency"
    - "you only look once unified real time object detection"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "orb slam a versatile and accurate monocular slam system"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "learning to generate facial depth maps"):
  with members:
    - "structured attention guided convolutional neural fields for monocular depth estimation"
    - "unsupervised learning of depth and ego motion from video"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "poseidon face from depth for driver pose estimation"
    - "learning depth from single monocular images using deep convolutional neural fields"
    - "learning representations for automatic colorization"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a two streamed network for estimating fine scaled depth maps from single rgb images"

Connected through hyperedge HE-3 (induced by source paper "multi range real time depth inference from a monocular stabilized footage using a fully convolutional neural network"):
  with members:
    - "end to end depth from motion with stabilized monocular videos"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"
    - "sfm net learning of structure and motion from video"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "computing the stereo matching cost with a convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "evolution of visual odometry techniques"):
  with members:
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "the tum vi benchmark for evaluating visual inertial odometry"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "the event camera dataset and simulator event based data for pose estimation visual odometry and slam"
    - "deepvo towards end to end visual odometry with deep recurrent convolutional neural networks"
    - "deep image homography estimation"
    - "end to end learning for self driving cars"
    - "deepvo a deep learning approach for monocular visual odometry"

Connected through hyperedge HE-5 (induced by source paper "end to end depth from motion with stabilized monocular videos"):
  with members:
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "rgbd datasets past present and future"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "computing the stereo matching cost with a convolutional neural network"

Connected through hyperedge HE-6 (induced by source paper "learning image relations with contrast association networks"):
  with members:
    - "convolutional neural network architecture for geometric matching"
    - "demon depth and motion network for learning monocular stereo"
    - "hadamard product for low rank bilinear pooling"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "optical flow estimation using a spatial pyramid network"
    - "cnn based patch matching for optical flow with thresholded hinge loss"
    - "learning to extract motion from videos in convolutional neural networks"
    - "fully trainable deep matching"

Connected through hyperedge HE-7 (induced by source paper "learning to extract motion from videos in convolutional neural networks"):
  with members:
    - "learning image representations equivariant to ego motion"
    - "evaluating two stream cnn for video classification"
    - "spatial transformer networks"
    - "flownet learning optical flow with convolutional networks"
    - "learning image representations tied to ego motion"
    - "modeling spatial temporal clues in a hybrid deep learning framework for video classification"
    - "two stream convolutional networks for action recognition in videos"

Connected through hyperedge HE-8 (induced by source paper "flownet learning optical flow with convolutional networks"):
  with members:
    - "computing the stereo matching cost with a convolutional neural network"
    - "epicflow edge preserving interpolation of correspondences for optical flow"
    - "adam a method for stochastic optimization"
    - "hypercolumns for object segmentation and fine grained localization"
    - "caffe convolutional architecture for fast feature embedding"
    - "learning to generate chairs with convolutional neural networks"
    - "descriptor matching with convolutional neural networks a comparison to sift"
    - "depth map prediction from a single image using a multi scale deep network"

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)