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 "flip federation support for long range low power internet of things protocols"):
  Abstract: There is growing interest in the Internet of Things (IoT) and especially Low-Power Wide Area Networks (LPWAN), which are rapidly being rolled-out globally. Within the LPWAN market, LoRaWAN is considered a leading solution which has achieved significant success. Despite the rapid uptake of LoRaWAN, scalability concerns arising from interference and contention are also growing. While the current LoRaWAN protocol includes basic techniques to deal with these problems, recent research has shown that these mechanisms are ineffective at large scale. This paper addresses this problem by proposing FLIP, a novel, fully distributed and open architecture for LoRaWAN, that transforms standard LoRa gateways into a federated network, while preserving the privacy and security properties of the original LoRaWAN architecture. FLIP tackles the scalability limitations of LoRaWAN using consensus-driven and localised resource sharing between gateways, while also providing inherent support for the roaming of LoRa devices across the federation. Critically, the FLIP architecture is fully backwards compatible with all existing LoRa gateways and requires no modifications to the firmware of LoRa end-devices, facilitating its rapid adoption.

Includes members:
  Member "low power wide area network analysis can lora scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance analysis of single cell adaptive data rate enabled lorawan")
  Member "low power wide area networks an overview":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "random access based reliable uplink communication and power transfer using dynamic power splitting")
  Member "understanding the limits of lorawan":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deepsense enabling carrier sense in low power wide area networks using deep learning")

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)