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: "dispsegnet leveraging semantics for end to end learning of disparity estimation from stereo imagery"
  Abstract: Recent work has shown that convolutional neural networks (CNNs) can be applied successfully in disparity estimation, but these methods still suffer from errors in regions of low-texture, occlusions and reflections. Concurrently, deep learning for semantic segmentation has shown great progress in recent years. In this paper, we design a CNN architecture that combines these two tasks to improve the quality and accuracy of disparity estimation with the help of semantic segmentation. Specifically, we propose a network structure in which these two tasks are highly coupled. One key novelty of this approach is the two-stage refinement process. Initial disparity estimates are refined with an embedding learned from the semantic segmentation branch of the network. The proposed model is trained using an unsupervised approach, in which images from one half of the stereo pair are warped and compared against images from the other camera. Another key advantage of the proposed approach is that a single network is capable of outputting disparity estimates and semantic labels. These outputs are of great use in autonomous vehicle operation; with real-time constraints being key, such performance improvements increase the viability of driving applications. Experiments on KITTI and Cityscapes datasets show that our model can achieve state-of-the-art results and that leveraging embedding learned from semantic segmentation improves the performance of disparity estimation.

Connected through hyperedge HE-1 (induced by source paper "real time semantic stereo matching"):
  with members:
    - "residual pyramid learning for single shot semantic segmentation"
    - "guided stereo matching"
    - "ga net guided aggregation net for end to end stereo matching"
    - "group wise correlation stereo network"
    - "fast scnn fast semantic segmentation network"
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "segstereo exploiting semantic information for disparity estimation"

Connected through hyperedge HE-2 (induced by source paper "listereo generate dense depth maps from lidar and stereo imagery"):
  with members:
    - "sparse and noisy lidar completion with rgb guidance and uncertainty"
    - "dfusenet deep fusion of rgb and sparse depth information for image guided dense depth completion"
    - "deep attention based classification network for robust depth prediction"
    - "on the importance of stereo for accurate depth estimation an efficient semi supervised deep neural network approach"
    - "hms net hierarchical multi scale sparsity invariant network for sparse depth completion"
    - "estimating depth from rgb and sparse sensing"
    - "pyramid stereo matching network"
    - "in defense of classical image processing fast depth completion on the cpu"

Connected through hyperedge HE-3 (induced by source paper "amnet deep atrous multiscale stereo disparity estimation networks"):
  with members:
    - "practical deep stereo pds toward applications friendly deep stereo matching"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "segstereo exploiting semantic information for disparity estimation"
    - "depth estimation via affinity learned with convolutional spatial propagation network"
    - "edgestereo a context integrated residual pyramid network for stereo matching"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid stereo matching network"
    - "stacked hourglass networks for human pose estimation"

Connected through hyperedge HE-4 (induced by source paper "scale adaptive neural dense features learning via hierarchical context aggregation"):
  with members:
    - "end to end learning of keypoint detector and descriptor for pose invariant 3d matching"
    - "hierarchical metric learning and matching for 2d and 3d geometric correspondences"
    - "pairwise relational networks for face recognition"
    - "the contextual loss for image transformation with non aligned data"
    - "deep metric learning with hierarchical triplet loss"
    - "pyramid stereo matching network"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "unsupervised learning of depth and ego motion from video"

Connected through hyperedge HE-5 (induced by source paper "semantic stereo for incidental satellite images"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "segstereo exploiting semantic information for disparity estimation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "3d geometry aware semantic labeling of outdoor street scenes"
    - "fast disparity estimation using dense networks"
    - "im2height height estimation from single monocular imagery via fully residual convolutional deconvolutional network"
    - "semantic3d net a new large scale point cloud classification benchmark"
    - "memory efficient implementation of densenets"

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)