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: "learning where to attend like a human driver"
  Abstract: Despite the advent of autonomous cars, it's likely - at least in the near future - that human attention will still maintain a central role as a guarantee in terms of legal responsibility during the driving task. In this paper we study the dynamics of the driver's gaze and use it as a proxy to understand related attentional mechanisms. First, we build our analysis upon two questions: where and what the driver is looking at? Second, we model the driver's gaze by training a coarse-to-fine convolutional network on short sequences extracted from the DR(eye)VE dataset. Experimental comparison against different baselines reveal that the driver's gaze can indeed be learnt to some extent, despite i) being highly subjective and ii) having only one driver's gaze available for each sequence due to the irreproducibility of the scene. Eventually, we advocate for a new assisted driving paradigm which suggests to the driver, with no intervention, where she should focus her attention.

Connected through hyperedge HE-1 (induced by source paper "see attend and brake an attention based saliency map prediction model for end to end driving"):
  with members:
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection a survey"
    - "predicting human eye fixations via an lstm based saliency attentive model"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "predicting driver attention in critical situations"
    - "end to end learning for self driving cars"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

Connected through hyperedge HE-2 (induced by source paper "looking at the right stuff guided semantic gaze for autonomous driving"):
  with members:
    - "see more know more unsupervised video object segmentation with co attention siamese networks"
    - "attention based dropout layer for weakly supervised object localization"
    - "dynamic traffic scene classification with space time coherence"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "salient object detection a survey"
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-3 (induced by source paper "dada a large scale benchmark and model for driver attention prediction in accidental scenarios"):
  with members:
    - "lates latent space distillation for teacher student driving policy learning"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "salient object detection in the deep learning era an in depth survey"
    - "unsupervised traffic accident detection in first person videos"
    - "deepvs a deep learning based video saliency prediction approach"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "yolov3 an incremental improvement"
    - "joint attention in driver pedestrian interaction from theory to practice"

Connected through hyperedge HE-4 (induced by source paper "periphery fovea multi resolution driving model guided by human attention"):
  with members:
    - "agil learning attention from human for visuomotor tasks"
    - "textual explanations for self driving vehicles"
    - "deep object centric policies for autonomous driving"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "predicting driver attention in critical situations"
    - "attention is all you need"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-5 (induced by source paper "dada 2000 can driving accident be predicted by driver attention analyzed by a benchmark"):
  with members:
    - "unsupervised traffic accident detection in first person videos"
    - "real world anomaly detection in surveillance videos"
    - "is it safe to drive an overview of factors challenges and datasets for driveability assessment in autonomous driving"
    - "anticipating traffic accidents with adaptive loss and large scale incident db"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "drive video analysis for the detection of traffic near miss incidents"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "predicting driver attention in critical situations"

Connected through hyperedge HE-6 (induced by source paper "joint attention in driver pedestrian interaction from theory to practice"):
  with members:
    - "when vehicles see pedestrians with phones a multi cue framework for recognizing phone based activities of pedestrians"
    - "video salient object detection via fully convolutional networks"
    - "agreeing to cross how drivers and pedestrians communicate"
    - "citypersons a diverse dataset for pedestrian detection"
    - "integrating three mechanisms of visual attention for active visual search"
    - "real time human motion capture with multiple depth cameras"
    - "hcp a flexible cnn framework for multi label image classification"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-7 (induced by source paper "deepvs a deep learning based video saliency prediction approach"):
  with members:
    - "revisiting video saliency a large scale benchmark and a new model"
    - "video salient object detection via fully convolutional networks"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "a deep multi level network for saliency prediction"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "shallow and deep convolutional networks for saliency prediction"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "recurrent mixture density network for spatiotemporal visual attention"

Connected through hyperedge HE-8 (induced by source paper "dynamics of driver s gaze explorations in behavior modeling maneuver prediction"):
  with members:
    - "what can be predicted from six seconds of driver glances"
    - "it s written all over your face full face appearance based gaze estimation"
    - "owl and lizard patterns of head pose and eye pose in driver gaze classification"
    - "car that knows before you do anticipating maneuvers via learning temporal driving models"

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)