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: "understanding the limits of lorawan"
  Abstract: The quick proliferation of LPWAN networks, being LoRaWAN one of the most adopted, raised the interest of the industry, network operators and facilitated the development of novel services based on large scale and simple network structures. LoRaWAN brings the desired ubiquitous connectivity to enable most of the outdoor IoT applications and its growth and quick adoption are real proofs of that. Yet the technology has some limitations that need to be understood in order to avoid over-use of the technology. In this article we aim to provide an impartial overview of what are the limitations of such technology, and in a comprehensive manner bring use case examples to show where the limits are.

Connected through hyperedge HE-1 (induced by source paper "deepsense enabling carrier sense in low power wide area networks using deep learning"):
  with members:
    - "deep learning methods for improved decoding of linear codes"
    - "deep image prior"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep complex networks"
    - "on deep learning based channel decoding"
    - "low power wide area networks an overview"
    - "eie efficient inference engine on compressed deep neural network"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-2 (induced by source paper "a comparative survey of lpwa networking"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"
    - "random access preamble design and detection for 3gpp narrowband iot systems"
    - "emerging wireless technologies in the internet of things a comparative study"
    - "understanding the iot connectivity landscape a contemporary m2m radio technology roadmap"
    - "internet of things iot a vision architectural elements and future directions"

Connected through hyperedge HE-3 (induced by source paper "grant free radio access iot networks scalability analysis in coexistence scenarios"):
  with members:
    - "grant free radio access for short packet communications over 5g networks"
    - "on lorawan scalability empirical evaluation of susceptibility to inter network interference"
    - "lorawan in the wild measurements from the things network"
    - "low power wide area network analysis can lora scale"
    - "e 2 mac energy efficient medium access for massive m2m communications"
    - "2d time frequency interference modelling using stochastic geometry for performance evaluation in low power wide area networks"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"

Connected through hyperedge HE-4 (induced by source paper "experimental study on low power wide area networks lpwan for mobile internet of things"):
  with members:
    - "mobile internet of things can uavs provide an energy efficient mobile architecture"
    - "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 "does bidirectional traffic do more harm than good in lorawan based lpwa networks"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

Connected through hyperedge HE-6 (induced by source paper "analysis of latency and mac layer performance for class a lorawan"):
  with members:
    - "on lorawan scalability empirical evaluation of susceptibility to inter network interference"
    - "a primer on 3gpp narrowband internet of things nb iot"

Connected through hyperedge HE-7 (induced by source paper "flip federation support for long range low power internet of things protocols"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

Connected through hyperedge HE-8 (induced by source paper "low power wide area networks an overview"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "a two prong approach to energy efficient wsns"
    - "a primer on 3gpp narrowband internet of things nb iot"
    - "mitigating inter network interference in lora networks"
    - "understanding the iot connectivity landscape a contemporary m2m radio technology roadmap"

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)