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: "clustering of driving scenarios using connected vehicle datasets"
  Abstract: Driving encounter classification and analysis can benefit autonomous vehicles to efficiently achieve a more smart decision. This paper presents an unsupervised learning framework to classify a wide range of driving encounters which compose of a pair of vehicles' GPS trajectories ordered by time. First, we develop five specific approaches, through integrating deep autoencoders with distance-based measures, to learn underlying representations of driving encounters in terms of both shape and distance, and we thoroughly compare and evaluate the performance of the five approaches. We then apply k-means clustering to categorize driving encounters into distinguishable groups based on the learned representations. Our proposed unsupervised learning framework for driving encounter classification is finally validated based on 2568 naturalistic driving encounters from connected vehicle datasets.

Connected through hyperedge HE-1 (induced by source paper "multi vehicle interaction scenarios generation with interpretable traffic primitives and gaussian process regression"):
  with members:
    - "understanding v2v driving scenarios through traffic primitives"
    - "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-2 (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"
    - "understanding v2v driving scenarios through traffic primitives"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-3 (induced by source paper "how to evaluate self driving testing ground a quantitative approach"):
  with members:
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "understanding v2v driving scenarios through traffic primitives"
    - "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 "measuring similarity of interactive driving behaviors using matrix profile"):
  with members:
    - "modeling multi vehicle interaction scenarios using gaussian random field"

Connected through hyperedge HE-5 (induced by source paper "understanding v2v driving scenarios through traffic primitives"):
  with members:
    - "learning and inferring a driver s braking action in car following scenarios"
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"
    - "discrete and continuous probabilistic anticipation for autonomous robots in urban environments"

Connected through hyperedge HE-6 (induced by source paper "an xcity optimization approach to designing proving grounds for connected and 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.RO(Robotics)