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: "lora throughput analysis with imperfect spreading factor orthogonality"
  Abstract: LoRa is one of the promising techniques for enabling Low Power Wide Area Networks (LPWANs) for future Internet-of-Things (IoT) devices. Although LoRa allows flexible adaptations of coverage and data rates, it is subject to intrinsic types of interferences: co-SF interferences where end-devices with the same Spreading Factors (SFs) are subject to collisions, and inter-SF interferences where end-devices with different SFs experience collisions. Most current works have considered perfect orthogonality among different SFs. In this work, we provide a theoretical analysis of the achievable LoRa throughput in uplink, where the capture conditions specific to LoRa are included. Results show the accuracy of our analysis despite approximations, and the throughput losses from imperfect SF orthogonality, under different SF allocations. Our analysis will enable the design of specific SF allocation mechanisms, in view of further throughput enhancements.

Connected through hyperedge HE-1 (induced by source paper "joint allocation strategies of power and spreading factors with imperfect orthogonality in lora networks"):
  with members:
    - "resource allocation in wireless powered iot networks"
    - "decoding superposed lora signals"
    - "scalability analysis of a lora network under imperfect orthogonality"
    - "low power wide area network analysis can lora scale"
    - "matching theory for future wireless networks fundamentals and applications"

Connected through hyperedge HE-2 (induced by source paper "analysis of lorawan uplink with multiple demodulating paths and capture effect"):
  with members:
    - "scalability analysis of a lora network under imperfect orthogonality"
    - "analysis of latency and mac layer performance for class a lorawan"
    - "low power wide area network analysis can lora scale"

Connected through hyperedge HE-3 (induced by source paper "ss5g collision resolution protocol for delay and energy efficient lora networks"):
  with members:
    - "decoding superposed lora signals"
    - "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"

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)