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 "dada 2000 can driving accident be predicted by driver attention analyzed by a benchmark"):
  Abstract: Driver attention prediction is currently becoming the focus in safe driving research community, such as the DR(eye)VE project and newly emerged Berkeley DeepDrive Attention (BDD-A) database in critical situations. In safe driving, an essential task is to predict the incoming accidents as early as possible. BDD-A was aware of this problem and collected the driver attention in laboratory because of the rarity of such scenes. Nevertheless, BDD-A focuses the critical situations which do not encounter actual accidents, and just faces the driver attention prediction task, without a close step for accident prediction. In contrast to this, we explore the view of drivers' eyes for capturing multiple kinds of accidents, and construct a more diverse and larger video benchmark than ever before with the driver attention and the driving accident annotation simultaneously (named as DADA-2000), which has 2000 video clips owning about 658,476 frames on 54 kinds of accidents. These clips are crowd-sourced and captured in various occasions (highway, urban, rural, and tunnel), weather (sunny, rainy and snowy) and light conditions (daytime and nighttime). For the driver attention representation, we collect the maps of fixations, saccade scan path and focusing time. The accidents are annotated by their categories, the accident window in clips and spatial locations of the crash-objects. Based on the analysis, we obtain a quantitative and positive answer for the question in this paper.

Includes members:
  Member "unsupervised traffic accident detection in first person videos":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios")
  Member "real world anomaly detection in surveillance videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "is it safe to drive an overview of factors challenges and datasets for driveability assessment in autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mvp unified motion and visual self supervised learning for large scale robotic navigation")
  Member "anticipating traffic accidents with adaptive loss and large scale incident db":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "intelligent intersection two stream convolutional networks for real time near accident detection in traffic video")
  Member "joint attention in driver pedestrian interaction from theory to practice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "looking to relations for future trajectory forecast")
  Member "drive video analysis for the detection of traffic near miss incidents":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligent intersection two stream convolutional networks for real time near accident detection in traffic video")
  Member "agreeing to cross how drivers and pedestrians communicate":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiple object forecasting predicting future object locations in diverse environments")
  Member "predicting driver attention in critical situations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "interaction graphs for object importance estimation in on road driving videos")

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)