human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "aads augmented autonomous driving simulation using data driven algorithms"):
  Abstract: Simulation systems have become essential to the development and validation of autonomous driving (AD) technologies. The prevailing state-of-the-art approach for simulation uses game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (the assets for simulation) remain manual tasks that can be costly and time consuming. In addition, CG images still lack the richness and authenticity of real-world images, and using CG images for training leads to degraded performance. Here, we present our augmented autonomous driving simulation (AADS). Our formulation augmented real-world pictures with a simulated traffic flow to create photorealistic simulation images and renderings. More specifically, we used LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generated plausible traffic flows for cars and pedestrians and composed them into the background. The composite images could be resynthesized with different viewpoints and sensor models (camera or LiDAR). The resulting images are photorealistic, fully annotated, and ready for training and testing of AD systems from perception to planning. We explain our system design and validate our algorithms with a number of AD tasks from detection to segmentation and predictions. Compared with traditional approaches, our method offers scalability and realism. Scalability is particularly important for AD simulations, and we believe that real-world complexity and diversity cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility of a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation.

Includes members:
  Member "the apolloscape open dataset for autonomous driving and its application":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "weighted average precision adversarial example detection in the visual perception of autonomous vehicles")
  Member "spg net segmentation prediction and guidance network for image inpainting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "guidance and evaluation semantic aware image inpainting for mixed scenes")
  Member "augmented lidar simulator for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how much real data do we actually need analyzing object detection performance using synthetic and real data")
  Member "heter sim heterogeneous multi agent systems simulation by interactive data driven optimization":
    also connected through other hyperedges:
      (none)
  Member "a lidar point cloud generator from a virtual world to autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "madan multi source adversarial domain aggregation network for domain adaptation")
  Member "trafficpredict trajectory prediction for heterogeneous traffic agents":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms")
  Member "sim4cv a photo realistic simulator for computer vision applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning to train with synthetic humans")
  Member "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a review of sensing and communication human factors and controller aspects for information aware connected and automated vehicles")

Question: Please tell me which class the source paper 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)