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 "providentia a large scale sensing system for the assistance of autonomous vehicles"):
  Abstract: The environmental perception of autonomous vehicles is not only limited by physical sensor ranges and algorithmic performance, but also occlusions degrade their understanding of the current traffic situation. This poses a great threat for safety, limits their driving speed and can lead to inconvenient maneuvers that decrease their acceptance. Intelligent Transportation Systems can help to alleviate these problems. By providing autonomous vehicles with additional detailed information about the current traffic in form of a digital model of their world, i.e. a digital twin, an Intelligent Transportation System can fill in the gaps in the vehicle's perception and enhance its field of view. However, detailed descriptions of implementations of such a system and working prototypes demonstrating its feasibility are scarce. In this work, we propose a hardware and software architecture to build such a reliable Intelligent Transportation System. We have implemented this system in the real world and show that it is able to create an accurate digital twin of an extended highway stretch. Furthermore, we provide this digital twin to an autonomous vehicle and demonstrate how it extends the vehicle's perception beyond the limits of its on-board sensors.

Includes members:
  Member "graph neural networks for modelling traffic participant interaction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "scale net scalable vehicle trajectory prediction network under random number of interacting vehicles via edge enhanced graph convolutional neural network")
  Member "targetless rotational auto calibration of radar and camera for intelligent transportation systems":
    also connected through other hyperedges:
      (none)
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "traffic danger recognition with surveillance cameras without training data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "training free monocular 3d event detection system for traffic surveillance")
  Member "understanding traffic density from large scale web camera data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey")
  Member "learning deep neural networks for vehicle re id with visual spatio temporal path proposals":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  Member "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dual convolutional lstm network for referring image segmentation")

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)