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: "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
  Abstract: Driving Scene understanding is a key ingredient for intelligent transportation systems. To achieve systems that can operate in a complex physical and social environment, they need to understand and learn how humans drive and interact with traffic scenes. We present the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments. The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. We provide a detailed analysis of HDD with a comparison to other driving datasets. A novel annotation methodology is introduced to enable research on driver behavior understanding from untrimmed data sequences. As the first step, baseline algorithms for driver behavior detection are trained and tested to demonstrate the feasibility of the proposed task.

Connected through hyperedge HE-1 (induced by source paper "sensor fusion gated recurrent fusion to learn driving behavior from temporal multimodal data"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised model adaptation for multimodal semantic segmentation"
    - "vlocnet deep multitask learning for semantic visual localization and odometry"
    - "online action detection in untrimmed streaming videos modeling and evaluation"
    - "learning to drive in a day"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "lstm a search space odyssey"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-2 (induced by source paper "overview of tools supporting planning for automated driving"):
  with members:
    - "liability ethics and culture aware behavior specification using rulebooks"
    - "self driving cars a survey"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "design space of behaviour planning for autonomous driving"
    - "a survey of autonomous driving common practices and emerging technologies"
    - "fiesta fast incremental euclidean distance fields for online motion planning of aerial robots"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"

Connected through hyperedge HE-3 (induced by source paper "who make drivers stop towards driver centric risk assessment risk object identification via causal inference"):
  with members:
    - "urban driving with conditional imitation learning"
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"
    - "causal confusion in imitation learning"
    - "grounding human to vehicle advice for self driving vehicles"
    - "goal oriented object importance estimation in on road driving videos"
    - "causal induction from visual observations for goal directed tasks"
    - "a self validation network for object level human attention estimation"

Connected through hyperedge HE-4 (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"
    - "predicting driver attention in critical situations"
    - "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-5 (induced by source paper "cmetric a driving behavior measure using centrality functions"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "graphrqi classifying driver behaviors using graph spectrums"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"

Connected through hyperedge HE-6 (induced by source paper "visuomotor understanding for representation learning of driving scenes"):
  with members:
    - "unsupervised learning of depth and ego motion from monocular video using 3d geometric constraints"
    - "mid level visual representations improve generalization and sample efficiency for learning active tasks"
    - "geometry aware deep network for single image novel view synthesis"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "mid level visual representations improve generalization and sample efficiency for learning visuomotor policies"
    - "stereo magnification learning view synthesis using multiplane images"
    - "deep view morphing"
    - "transformation grounded image generation network for novel 3d view synthesis"

Connected through hyperedge HE-7 (induced by source paper "exploring applications of deep reinforcement learning for real world autonomous driving systems"):
  with members:
    - "on first order meta learning algorithms"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "hierarchical imitation and reinforcement learning"
    - "learning to drive in a day"

Connected through hyperedge HE-8 (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"

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)