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: "scalability analysis of large scale lorawan networks in ns 3"
  Abstract: As LoRaWAN networks are actively being deployed in the field, it is important to comprehend the limitations of this Low Power Wide Area Network technology. Previous work has raised questions in terms of the scalability and capacity of LoRaWAN networks as the number of end devices grows to hundreds or thousands per gateway. Some works have modeled LoRaWAN networks as pure ALOHA networks, which fails to capture important characteristics such as the capture effect and the effects of interference. Other works provide a more comprehensive model by relying on empirical and stochastic techniques. This work uses a different approach where a LoRa error model is constructed from extensive complex baseband bit error rate simulations and used as an interference model. The error model is combined with the LoRaWAN MAC protocol in an ns-3 module that enables to study multi channel, multi spreading factor, multi gateway, bi-directional LoRaWAN networks with thousands of end devices. Using the lorawan ns-3 module, a scalability analysis of LoRaWAN shows the detrimental impact of downstream traffic on the delivery ratio of confirmed upstream traffic. The analysis shows that increasing gateway density can ameliorate but not eliminate this effect, as stringent duty cycle requirements for gateways continue to limit downstream opportunities.

Connected through hyperedge HE-1 (induced by source paper "a thorough study of lorawan performance under different parameter settings"):
  with members:
    - "employing p csma on a lora network simulator"
    - "uncoordinated access schemes for the iot approaches regulations and performance"
    - "does bidirectional traffic do more harm than good in lorawan based lpwa networks"
    - "analysis of latency and mac layer performance for class a lorawan"
    - "m2m massive access in lte rach performance evaluation in a smart city scenario"
    - "internet of things applications and challenges in technology and standardization"

Connected through hyperedge HE-2 (induced by source paper "slotted aloha on lorawan design analysis and deployment"):
  with members:
    - "slotted aloha overlay on lorawan a distributed synchronization approach"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-3 (induced by source paper "a complete lorawan model for single gateway scenarios"):
  with members:
    - "a thorough study of lorawan performance under different parameter settings"
    - "does bidirectional traffic do more harm than good in lorawan based lpwa networks"
    - "analysis of latency and mac layer performance for class a lorawan"
    - "low power wide area network analysis can lora scale"

Connected through hyperedge HE-4 (induced by source paper "on the lora modulation for iot waveform properties and spectral analysis"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"

Connected through hyperedge HE-5 (induced by source paper "achieving max min throughput in lora networks"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "stochastic analysis of non slotted aloha in wireless ad hoc networks"

Connected through hyperedge HE-6 (induced by source paper "slotted aloha overlay on lorawan a distributed synchronization approach"):
  with members:
    - "lightweight synchronization algorithm with self calibration for industrial lora sensor networks"
    - "low power wide area network analysis can lora scale"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"

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.NI(Networking and Internet Architecture)