human: Given a node-centered hypergraph: <hypergraph>, where nodes represent authors and hyperedges represent groups of authors who co-authored the same paper.

Details of the hypergraph:
Center node: "Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement"
  Abstract: Significant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs). While absolute features, such as edges and textures, could be effectively extracted, the depth constraint of neighboring pixels, namely relative features, has been mostly ignored by recent CNN-based methods. To overcome this limitation, we explicitly model the relationships of different image locations with an affinity layer and combine absolute and relative features in an end-to-end network. In addition, we consider prior knowledge that major depth changes lie in the vertical direction, and thus, it is beneficial to capture long-range vertical features for refined depth estimation. In the proposed algorithm we introduce vertical pooling to aggregate image features vertically to improve the depth accuracy. Furthermore, since the Lidar depth ground truth is quite sparse, we enhance the depth labels by generating high-quality dense depth maps with off-the-shelf stereo matching method taking left-right image pairs as input. We also integrate multi-scale structure in our network to obtain global understanding of the image depth and exploit residual learning to help depth refinement. We demonstrate that the proposed algorithm performs favorably against state-of-the-art methods both qualitatively and quantitatively on the KITTI driving dataset.

Connected through hyperedge HE-1 (induced by source paper "Are we ready for autonomous driving? The KITTI vision benchmark suite"):
  with members:
    - "End-To-End Ego Lane Estimation Based on Sequential Transfer Learning for Self-Driving Cars"
    - "Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation"
    - "SegFlow: Joint Learning for Video Object Segmentation and Optical Flow"
    - "Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames"
    - "WildDash - Creating Hazard-Aware Benchmarks"
    - "Structured Domain Randomization: Bridging the Reality Gap by Context-Aware Synthetic Data."
    - "Developing Vision-based and Cooperative Vehicular Embedded Systems for Enhancing Road Monitoring Services"
    - "A Flexible High-Resolution Real-Time Low-Power Stereo Vision Engine"

Question: Please tell me which research area the center author belongs to. The 4 classes are: Computer science, Mathematics, Engineering, Economics, Psychology, Business. Directly output the class name.

gpt: Computer science