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 "empirical analysis of client based network quality prediction in vehicular multi mno networks"):
  Abstract: Multi-Mobile Network Operator (MNO) networking is a promising method to exploit the joint force of multiple available cellular data connections within vehicular networks. By applying anticipatory communication principles, data transmissions can dynamically utilize the mobile network with the highest estimated network performance in order to achieve improvements in data rate, resource efficiency, and reliability. In this paper, we present the results of a comprehensive real-world measurement campaign in public cellular networks in different scenarios and analyze the performance of online data rate prediction based on multiple machine learning models and data aggregation strategies. It is shown that multi-MNO approaches are able to achieve significant benefits for all considered network quality and end-to-end indicators even in the presence of a single dominant MNO. However, the analysis points out that anticipatory multi-MNO communication requires the consideration of MNO-specific machine learning models since the impact of the different features is highly depending on the configuration of the network infrastructure.

Includes members:
  Member "system of systems modeling analysis and optimization of hybrid vehicular traffic":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "boosting vehicle to cloud communication by machine learning enabled context prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "machine learning based context predictive car to cloud communication using multi layer connectivity maps for upcoming 5g networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a reinforcement learning approach for efficient opportunistic vehicle to cloud data transfer")
  Member "machine learning based uplink transmission power prediction for lte and upcoming 5g networks using passive downlink indicators":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "b a t mobile leveraging mobility control knowledge for efficient routing in mobile robotic networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lightweight simulation of hybrid aerial and ground based vehicular communication networks")
  Member "kernel based adaptive online reconstruction of coverage maps with side information":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "boosting vehicle to cloud communication by machine learning enabled context prediction")
  Member "a survey of anticipatory mobile networking context based classification prediction methodologies and optimization techniques":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")

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.NI(Networking and Internet Architecture)