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: "automated evaluation of semantic segmentation robustness for autonomous driving"
  Abstract: One of the fundamental challenges in the design of perception systems for autonomous vehicles is validating the performance of each algorithm under a comprehensive variety of operating conditions. In the case of vision-based semantic segmentation, there are known issues when encountering new scenarios that are sufficiently different to the training data. In addition, even small variations in environmental conditions, such as illumination and precipitation, can affect the classification performance of the segmentation model. Given the reliance on visual information, these effects often translate into poor semantic pixel classification which can potentially lead to catastrophic consequences when driving autonomously. This paper presents a novel method for analyzing the robustness of semantic segmentation models and provides a number of metrics to evaluate the classification performance over a variety of environmental conditions. The process incorporates an additional sensor (lidar) to automate the process and improve the system integrity, eliminating the need for labor-intensive hand labeling of validation data. The experimental results are presented based on multiple datasets collected at different times of the year with different environmental conditions. We extract the "Road" class using the lidar to demonstrate the concepts, but this could be extended to other classes with different feature detection algorithms. These results show that the semantic segmentation performance varies depending on the weather, camera parameters, and existence of shadows. The results also demonstrate how the metrics can be used to compare and validate the performance after making improvements to a model, and compare the performance of different networks.

Connected through hyperedge HE-1 (induced by source paper "semantic sensor fusion from camera to sparse lidar information"):
  with members:
    - "adapting semantic segmentation models for changes in illumination and camera perspective"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

Connected through hyperedge HE-2 (induced by source paper "probabilistic egocentric motion correction of lidar point cloud and projection to camera images for moving platforms"):
  with members:
    - "automatic extrinsic calibration between a camera and a 3d lidar using 3d point and plane correspondences"
    - "learning a bias correction for lidar only motion estimation"
    - "lidar point clouds correction acquired from a moving car based on can bus data"

Connected through hyperedge HE-3 (induced by source paper "automatic extrinsic calibration between a camera and a 3d lidar using 3d point and plane correspondences"):
  with members:
    - "lidar camera calibration using 3d 3d point correspondences"
    - "a novel method for extrinsic calibration of a 2 d laser rangefinder and a camera"

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)