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: "trafficnet an open naturalistic driving scenario library"
  Abstract: The enormous efforts spent on collecting naturalistic driving data in the recent years has resulted in an expansion of publicly available traffic datasets, which has the potential to assist the development of the self-driving vehicles. However, we found that many of the attempts to utilize these datasets have failed in practice due to a lack of usability concern from the organizations that host these collected data. For example, extracting data associated with certain critical conditions from naturalistic driving data organized in chronological order may not be convenient for a vehicle engineer that doesn't have big data analytics experiences. #R##N#To address the general usability challenges of these publicly available traffic datasets, we propose TrafficNet, a large-scale and extensible library of naturalistic driving scenarios, aiming at bridging the gap between research datasets and practically usable information for vehicle engineers and researchers. The proposed web-based driving scenario database preprocesses massive raw traffic data collected in chronological order into an organized scenario-based dataset by applying a set of categorization algorithms to label the naturalistic driving data with six different critical driving scenarios. TrafficNet opens not only the scenario library but also the source code of these categorization methods to the public, which will foster more sophisticated and accurate scenario-based categorization algorithms to advance the intelligent transportation research. The source code and the scenario database can be accessed at this https URL.

Connected through hyperedge HE-1 (induced by source paper "a behavior driven approach for sampling rare event situations for autonomous vehicles"):
  with members:
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"

Connected through hyperedge HE-2 (induced by source paper "cluster naturalistic driving encounters using deep unsupervised learning"):
  with members:
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "autoencoder regularized network for driving style representation learning"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"
    - "empirical study of dsrc performance based on safety pilot model deployment data"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"
    - "accelerated evaluation of automated vehicles in car following maneuvers"

Connected through hyperedge HE-3 (induced by source paper "a tempt to unify heterogeneous driving databases using traffic primitives"):
  with members:
    - "extracting traffic primitives directly from naturalistically logged data for self driving applications"
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"

Connected through hyperedge HE-4 (induced by source paper "extracting traffic primitives directly from naturalistically logged data for self driving applications"):
  with members:
    - "driving style analysis using primitive driving patterns with bayesian nonparametric approaches"
    - "evaluation of a semi autonomous lane departure correction system using naturalistic driving data"

Connected through hyperedge HE-5 (induced by source paper "a versatile approach to evaluating and testing automated vehicles based on kernel methods"):
  with members:
    - "an accelerated testing approach for automated vehicles with background traffic described by joint distributions"
    - "accelerated evaluation of automated vehicles using piecewise mixture models"
    - "accelerated evaluation of automated vehicles in car following maneuvers"

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.CY(Computers and Society)