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: "understanding v2v driving scenarios through traffic primitives"
  Abstract: Semantically understanding complex drivers' encountering behavior, wherein two or multiple vehicles are spatially close to each other, does potentially benefit autonomous car's decision-making design. This paper presents a framework of analyzing various encountering behaviors through decomposing driving encounter data into small building blocks, called driving primitives, using nonparametric Bayesian learning (NPBL) approaches, which offers a flexible way to gain an insight into the complex driving encounters without any prerequisite knowledge. The effectiveness of our proposed primitive-based framework is validated based on 976 naturalistic driving encounters, from which more than 4000 driving primitives are learned using NPBL - a sticky HDP-HMM, combined a hidden Markov model (HMM) with a hierarchical Dirichlet process (HDP). After that, a dynamic time warping method integrated with k-means clustering is then developed to cluster all these extracted driving primitives into groups. Experimental results find that there exist 20 kinds of driving primitives capable of representing the basic components of driving encounters in our database. This primitive-based analysis methodology potentially reveals underlying information of vehicle-vehicle encounters for self-driving applications.

Connected through hyperedge HE-1 (induced by source paper "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"):
  with members:
    - "generic prediction architecture considering both rational and irrational driving behaviors"
    - "human motion trajectory prediction a survey"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "an lstm network for highway trajectory prediction"
    - "deep imitative models for flexible inference planning and control"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"

Connected through hyperedge HE-2 (induced by source paper "multi vehicle interaction scenarios generation with interpretable traffic primitives and gaussian process regression"):
  with members:
    - "clustering of driving scenarios using connected vehicle datasets"
    - "end to end learning for self driving cars"
    - "a survey of motion planning and control techniques for self driving urban vehicles"
    - "discrete and continuous probabilistic anticipation for autonomous robots in urban environments"

Connected through hyperedge HE-3 (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"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-4 (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"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-5 (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"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-6 (induced by source paper "an accelerated approach to safely and efficiently test pre produced autonomous vehicles on public streets"):
  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.LG(Machine Learning)