human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "an accelerated approach to safely and efficiently test pre produced autonomous vehicles on public streets"):
  Abstract: Various automobile and mobility companies, for instance, Ford, Uber, and Waymo, are currently testing their pre-produced autonomous vehicle (AV) fleets on the public roads. However, due to the rareness of the safety-critical cases and, effectively, unlimited number of possible traffic scenarios, these on-road testing efforts have been acknowledged as tedious, costly, and risky. In this study, we propose Accelerated Deployment framework to safely and efficiently estimate the AVs performance on public streets. We showed that by appropriately addressing the gradual accuracy improvement and adaptively selecting meaningful and safe environment under which the AV is deployed, the proposed framework yield to highly accurate estimation with much faster evaluation time, and more importantly, lower deployment risk. Our findings provide an answer to the currently heated and active discussions on how to properly test AV performance on public roads so as to achieve safe, efficient, and statistically-reliable testing framework for AV technologies.

Includes members:
  Member "extracting traffic primitives directly from naturalistically logged data for self driving applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spatiotemporal learning of multivehicle interaction patterns in lane change scenarios")
  Member "understanding v2v driving scenarios through traffic primitives":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps")

Question: Please tell me which class the source paper 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)