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: "SegFlow: Joint Learning for Video Object Segmentation and Optical Flow"
  Abstract: This paper proposes an end-to-end trainable network, SegFlow, for simultaneously predicting pixel-wise object segmentation and optical flow in videos. The proposed SegFlow has two branches where useful information of object segmentation and optical flow is propagated bidirectionally in a unified framework. The segmentation branch is based on a fully convolutional network, which has been proved effective in image segmentation task, and the optical flow branch takes advantage of the FlowNet model. The unified framework is trained iteratively offline to learn a generic notion, and fine-tuned online for specific objects. Extensive experiments on both the video object segmentation and optical flow datasets demonstrate that introducing optical flow improves the performance of segmentation and vice versa, against the state-of-the-art algorithms.

Connected through hyperedge HE-1 (induced by source paper "Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions"):
  with members:
    - "Category independent object proposals"
    - "Unsupervised pixel-level video foreground object segmentation via shortest path algorithm"
    - "First International Workshop on Video Segmentation - Panel Discussion"

Connected through hyperedge HE-2 (induced by source paper "Video Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting."):
  with members:
    (none)

Connected through hyperedge HE-3 (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"
    - "Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames"
    - "WildDash - Creating Hazard-Aware Benchmarks"
    - "Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation"):
  with members:
    - "HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion"
    - "Are we ready for autonomous driving? The KITTI vision benchmark suite"
    - "Optical Flow Estimation with Channel Constancy"
    - "Coarse-to-Fine Minimization of Some Common Nonconvexities"
    - "Optical Flow with Geometric Occlusion Estimation and Fusion of Multiple Frames"

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