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 a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  Abstract: Driver attention prediction has recently absorbed increasing attention in traffic scene understanding and is prone to be an essential problem in vision-centered and human-like driving systems. This work, different from other attempts, makes an attempt to predict the driver attention in accidental scenarios containing normal, critical and accidental situations simultaneously. However, challenges tread on the heels of that because of the dynamic traffic scene, intricate and imbalanced accident categories. With the hypothesis that driver attention can provide a selective role of crash-object for assisting driving accident detection or prediction, this paper designs a multi-path semantic-guided attentive fusion network (MSAFNet) that learns the spatio-temporal semantic and scene variation in prediction. For fulfilling this, a large-scale benchmark with 2000 video sequences (named as DADA-2000) is contributed with laborious annotation for driver attention (fixation, saccade, focusing time), accident objects/intervals, as well as the accident categories, and superior performance to state-of-the-arts are provided by thorough evaluations. As far as we know, this is the first comprehensive and quantitative study for the human-eye sensing exploration in accidental scenarios. DADA-2000 is available at this https URL.

Includes members:
  Member "lates latent space distillation for teacher student driving policy learning":
    also connected through other hyperedges:
      (none)
  Member "periphery fovea multi resolution driving model guided by human attention":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficiently guiding imitation learning algorithms with human gaze")
  Member "salient object detection in the deep learning era an in depth survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spatiotemporal knowledge distillation for efficient estimation of aerial video saliency")
  Member "unsupervised traffic accident detection in first person videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dada 2000 can driving accident be predicted by driver attention analyzed by a benchmark")
  Member "deepvs a deep learning based video saliency prediction approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stavis spatio temporal audiovisual saliency network")
  Member "saliency prediction in the deep learning era an empirical investigation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unified image and video saliency modeling")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "joint attention in driver pedestrian interaction from theory to practice":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "looking to relations for future trajectory forecast")

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)