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 the driver s focus of attention the dr eye ve project"
  Abstract: In this work we aim to predict the driver's focus of attention. The goal is to estimate what a person would pay attention to while driving, and which part of the scene around the vehicle is more critical for the task. To this end we propose a new computer vision model based on a multi-branch deep architecture that integrates three sources of information: raw video, motion and scene semantics. We also introduce DR(eye)VE, the largest dataset of driving scenes for which eye-tracking annotations are available. This dataset features more than 500,000 registered frames, matching ego-centric views (from glasses worn by drivers) and car-centric views (from roof-mounted camera), further enriched by other sensors measurements. Results highlight that several attention patterns are shared across drivers and can be reproduced to some extent. The indication of which elements in the scene are likely to capture the driver's attention may benefit several applications in the context of human-vehicle interaction and driver attention analysis.

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"
    - "predicting driver attention in critical situations"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-2 (induced by source paper "driving datasets literature review"):
  with members:
    - "eurocity persons a novel benchmark for person detection in traffic scenes"
    - "blvd building a large scale 5d semantics benchmark for autonomous driving"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "object detection in 20 years a survey"
    - "deep multi modal object detection and semantic segmentation for autonomous driving datasets methods and challenges"
    - "the streetlearn environment and dataset"

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 "leveraging human guidance for deep reinforcement learning tasks"):
  with members:
    - "imitation learning from video by leveraging proprioception"
    - "atari head atari human eye tracking and demonstration dataset"
    - "recent advances in imitation learning from observation"
    - "imitating latent policies from observation"
    - "an algorithmic perspective on imitation learning"
    - "agil learning attention from human for visuomotor tasks"
    - "ordered preference elicitation strategies for supporting multi objective decision making"
    - "learning actionable representations from visual observations"

Connected through hyperedge HE-5 (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-6 (induced by source paper "dynamic traffic scene classification with space time coherence"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "don t look back robustifying place categorization for viewpoint and condition invariant place recognition"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "rethinking the faster r cnn architecture for temporal action localization"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "traffic scene recognition based on deep cnn and vlad spatial pyramids"

Connected through hyperedge HE-7 (induced by source paper "atari head atari human eye tracking and demonstration dataset"):
  with members:
    - "gaze training by modulated dropout improves imitation learning"
    - "leveraging human guidance for deep reinforcement learning tasks"
    - "minerl a large scale dataset of minecraft demonstrations"
    - "periphery fovea multi resolution driving model guided by human attention"
    - "free lunch saliency via attention in atari agents"
    - "pre training with non expert human demonstration for deep reinforcement learning"
    - "agil learning attention from human for visuomotor tasks"
    - "bdd100k a diverse driving video database with scalable annotation tooling"

Connected through hyperedge HE-8 (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"
    - "predicting driver attention in critical situations"
    - "an analysis of scale invariance in object detection snip"
    - "dynamic zoom in network for fast object detection in large images"

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)