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: "car that knows before you do anticipating maneuvers via learning temporal driving models"
  Abstract: Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. #R##N#In this work we anticipate driving maneuvers a few seconds before they occur. For this purpose we equip a car with cameras and a computing device to capture the driving context from both inside and outside of the car. We propose an Autoregressive Input-Output HMM to model the contextual information alongwith the maneuvers. We evaluate our approach on a diverse data set with 1180 miles of natural freeway and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80\% F1-score in real-time.

Connected through hyperedge HE-1 (induced by source paper "towards accurate vehicle behaviour classification with multi relational graph convolutional networks"):
  with members:
    - "dynamic traffic scene classification with space time coherence"
    - "learning category specific mesh reconstruction from image collections"
    - "deep functional maps structured prediction for dense shape correspondence"
    - "attention is all you need"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "mask r cnn"
    - "syncspeccnn synchronized spectral cnn for 3d shape segmentation"
    - "learning features by watching objects move"

Connected through hyperedge HE-2 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks"):
  with members:
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "spatio temporal action graph networks"
    - "learning actor relation graphs for group activity recognition"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "classifying collisions with spatio temporal action graph networks"
    - "semi supervised learning fusion of self supervised supervised learning and multimodal cues for tactical driver behavior detection"

Connected through hyperedge HE-3 (induced by source paper "what would you expect anticipating egocentric actions with rolling unrolling lstms and modality attention"):
  with members:
    - "adding attentiveness to the neurons in recurrent neural networks"
    - "attention is all we need nailing down object centric attention for egocentric activity recognition"
    - "when will you do what anticipating temporal occurrences of activities"
    - "lsta long short term attention for egocentric action recognition"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "forecasting hand and object locations in future frames"
    - "am i done predicting action progress in videos"
    - "encouraging lstms to anticipate actions very early"

Connected through hyperedge HE-4 (induced by source paper "peeking into the future predicting future person activities and locations in videos"):
  with members:
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "a transferable pedestrian motion prediction model for intersections with different geometries"
    - "understanding human behaviors in crowds by imitating the decision making process"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "scene lstm a model for human trajectory prediction"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "relation networks for object detection"
    - "future person localization in first person videos"

Connected through hyperedge HE-5 (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-6 (induced by source paper "mcity data collection for automated vehicles study"):
  with members:
    - "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "an optimal lidar configuration approach for self driving cars"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "developing robot driver etiquette based on naturalistic human driving behavior"
    - "toward driving scene understanding a dataset for learning driver behavior and causal reasoning"
    - "pseudo lidar from visual depth estimation bridging the gap in 3d object detection for autonomous driving"
    - "orthographic feature transform for monocular 3d object detection"

Connected through hyperedge HE-7 (induced by source paper "learning driving decisions by imitating drivers control behaviors"):
  with members:
    - "combining optimal control and learning for visual navigation in novel environments"
    - "cirl controllable imitative reinforcement learning for vision based self driving"
    - "deep imitative models for flexible inference planning and control"
    - "learning from demonstration in the wild"
    - "adversarial attacks on neural network policies"
    - "carla an open urban driving simulator"
    - "imitating driver behavior with generative adversarial networks"
    - "a general safety framework for learning based control in uncertain robotic systems"

Connected through hyperedge HE-8 (induced by source paper "real time driver state monitoring using a cnn based spatio temporal approach"):
  with members:
    - "motion fused frames data level fusion strategy for hand gesture recognition"
    - "real time distracted driver posture classification"
    - "brain4cars car that knows before you do via sensory fusion deep learning architecture"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "imagenet large scale visual recognition challenge"

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)