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: "single view and multi view depth fusion"
  Abstract: Dense and accurate 3-D mapping from a monocular sequence is a key technology for several applications and still an open research area. This letter leverages recent results on single-view convolutional network (CNN)-based depth estimation and fuses them with multiview depth estimation. Both approaches present complementary strengths. Multiview depth is highly accurate but only in high-texture areas and high-parallax cases. Single-view depth captures the local structure of midlevel regions, including texture-less areas, but the estimated depth lacks global coherence. The single and multiview fusion we propose is challenging in several aspects. First, both depths are related by a deformation that depends on the image content. Second, the selection of multiview points of high accuracy might be difficult for low-parallax configurations. We present contributions for both problems. Our results in the public datasets of NYUv2 and TUM shows that our algorithm outperforms the individual single and multiview approaches. A video showing the key aspects of mapping in our single and multiview depth proposal is available at https://youtu.be/ipc5HukTb4k.

Connected through hyperedge HE-1 (induced by source paper "cam convs camera aware multi scale convolutions for single view depth"):
  with members:
    - "implicit 3d orientation learning for 6d object detection from rgb images"
    - "deeptam deep tracking and mapping"
    - "deep attention based classification network for robust depth prediction"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "just in time reconstruction inpainting sparse maps using single view depth predictors as priors"
    - "deep ordinal regression network for monocular depth estimation"
    - "deep depth completion of a single rgb d image"
    - "deepmvs learning multi view stereopsis"

Connected through hyperedge HE-2 (induced by source paper "mono sf multi view geometry meets single view depth for monocular scene flow estimation of dynamic traffic scenes"):
  with members:
    - "dense depth estimation of a complex dynamic scene without explicit 3d motion estimation"
    - "deep rigid instance scene flow"
    - "lightweight probabilistic deep networks"
    - "unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction"
    - "every pixel counts unsupervised geometry learning with holistic 3d motion understanding"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "deep ordinal regression network for monocular depth estimation"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"

Connected through hyperedge HE-3 (induced by source paper "sdf man semi supervised disparity fusion with multi scale adversarial networks"):
  with members:
    - "generative adversarial networks an overview"
    - "pyramid stereo matching network"
    - "improved training of wasserstein gans"
    - "unsupervised monocular depth estimation with left right consistency"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "image to image translation with conditional adversarial networks"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "depth map estimation for free viewpoint television"):
  with members:
    - "hole filling with multiple reference views in dibr view synthesis"
    - "occlusion model guided antiocclusion depth estimation in light field"

Connected through hyperedge HE-5 (induced by source paper "structural and object detection for phosphene images"):
  with members:
    - "panoroom from the sphere to the 3d layout"
    - "deep learning features at scale for visual place recognition"
    - "towards lightweight convolutional neural networks for object detection"
    - "fully convolutional instance aware semantic segmentation"
    - "instance aware semantic segmentation via multi task network cascades"
    - "fast r cnn"
    - "multimodal deep learning for robust rgb d object recognition"

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)