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: "predicting driver attention in critical situations"
  Abstract: Robust driver attention prediction for critical situations is a challenging computer vision problem, yet essential for autonomous driving. Because critical driving moments are so rare, collecting enough data for these situations is difficult with the conventional in-car data collection protocol---tracking eye movements during driving. Here, we first propose a new in-lab driver attention collection protocol and introduce a new driver attention dataset, Berkeley DeepDrive Attention (BDD-A) dataset, which is built upon braking event videos selected from a large-scale, crowd-sourced driving video dataset. We further propose Human Weighted Sampling (HWS) method, which uses human gaze behavior to identify crucial frames of a driving dataset and weights them heavily during model training. With our dataset and HWS, we built a driver attention prediction model that outperforms the state-of-the-art and demonstrates sophisticated behaviors, like attending to crossing pedestrians but not giving false alarms to pedestrians safely walking on the sidewalk. Its prediction results are nearly indistinguishable from ground-truth to humans. Although only being trained with our in-lab attention data, the model also predicts in-car driver attention data of routine driving with state-of-the-art accuracy. This result not only demonstrates the performance of our model but also proves the validity and usefulness of our dataset and data collection protocol.

Connected through hyperedge HE-1 (induced by source paper "interaction graphs for object importance estimation in on road driving videos"):
  with members:
    - "goal oriented object importance estimation in on road driving videos"
    - "deeply supervised salient object detection with short connections"
    - "graph r cnn for scene graph generation"
    - "videos as space time region graphs"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-2 (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"
    - "learning where to attend like a human driver"
    - "end to end learning for self driving cars"
    - "deepgaze ii reading fixations from deep features trained on object recognition"

Connected through hyperedge HE-3 (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-4 (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-5 (induced by source paper "goal oriented object importance estimation in on road driving videos"):
  with members:
    - "startnet online detection of action start in untrimmed videos"
    - "learning rich features for image manipulation detection"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "temporal recurrent networks for online action detection"
    - "interpretable learning for self driving cars by visualizing causal attention"
    - "an analysis of scale invariance in object detection snip"
    - "dynamic zoom in network for fast object detection in large images"
    - "predicting the driver s focus of attention the dr eye ve project"

Connected through hyperedge HE-6 (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"
    - "attention is all you need"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "learning where to attend like a human driver"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-7 (induced by source paper "eyecar modeling the visual attention allocation of drivers in semi autonomous vehicles"):
  with members:
    - "a deep reinforcement learning based driving policy for autonomous road vehicles"
    - "driving with style inverse reinforcement learning in general purpose planning for automated driving"
    - "agil learning attention from human for visuomotor tasks"
    - "the effect of whole body haptic feedback on driver s perception in negotiating a curve"
    - "joint attention in driver pedestrian interaction from theory to practice"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "predicting the driver s focus of attention the dr eye ve project"
    - "learning to drive using inverse reinforcement learning and deep q networks"

Connected through hyperedge HE-8 (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 the driver s focus of attention the dr eye ve project"

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)