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: "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"
  Abstract: Analysis and recognition of driving styles are profoundly important to intelligent transportation and vehicle calibration. This paper presents a novel driving style analysis framework using the primitive driving patterns learned from naturalistic driving data. In order to achieve this, first, a Bayesian nonparametric learning method based on a hidden semi-Markov model (HSMM) is introduced to extract primitive driving patterns from time series driving data without prior knowledge of the number of these patterns. In the Bayesian nonparametric approach, we utilize a hierarchical Dirichlet process (HDP) instead of learning the unknown number of smooth dynamical modes of HSMM, thus generating the primitive driving patterns. Each primitive pattern is clustered and then labeled using behavioral semantics according to drivers' physical and psychological perception thresholds. For each driver, 75 primitive driving patterns in car-following scenarios are learned and semantically labeled. In order to show the HDP-HSMM's utility to learn primitive driving patterns, other two Bayesian nonparametric approaches, HDP-HMM and sticky HDP-HMM, are compared. The naturalistic driving data of 18 drivers were collected from the University of Michigan Safety Pilot Model Deployment (SPDM) database. The individual driving styles are discussed according to distribution characteristics of the learned primitive driving patterns and also the difference in driving styles among drivers are evaluated using the Kullback-Leibler divergence. The experiment results demonstrate that the proposed primitive pattern-based method can allow one to semantically understand driver behaviors and driving styles.

Connected through hyperedge HE-1 (induced by source paper "spatiotemporal learning of multivehicle interaction patterns in lane change scenarios"):
  with members:
    - "incremental learning of motion primitives for pedestrian trajectory prediction at intersections"
    - "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"
    - "recurrent attentive neural process for sequential data"
    - "a general framework of learning multi vehicle interaction patterns from videos"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "the highd dataset a drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems"

Connected through hyperedge HE-2 (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-3 (induced by source paper "detecting inter sectional accuracy differences in driver drowsiness detection algorithms"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "identity mappings in deep residual networks"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "rethinking the inception architecture for computer vision"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "recurrent neural processes"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "attentive neural processes"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "the functional neural process"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "neural processes"

Connected through hyperedge HE-5 (induced by source paper "how to evaluate proving grounds for self driving a quantitative approach"):
  with members:
    - "clustering of driving encounter scenarios using connected vehicle trajectories"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "understanding v2v driving scenarios through traffic primitives"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-6 (induced by source paper "a general framework of learning multi vehicle interaction patterns from videos"):
  with members:
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "yolov3 an incremental improvement"
    - "clustering of driving scenarios using connected vehicle datasets"
    - "understanding v2v driving scenarios through traffic primitives"

Connected through hyperedge HE-7 (induced by source paper "how to evaluate self driving testing ground a quantitative approach"):
  with members:
    - "clustering of driving scenarios using connected vehicle datasets"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "understanding v2v driving scenarios through traffic primitives"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-8 (induced by source paper "estimating risk levels of driving scenarios through analysis of driving styles for autonomous vehicles"):
  with members:
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"

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)