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: "fair adaptive data rate allocation and power control in lorawan"
  Abstract: In this paper, we present results of a study of the data rate fairness among nodes within a LoRaWAN cell. Since LoRa/LoRaWAN supports various data rates, we firstly derive the fairest ratios of deploying each data rate within a cell for a fair collision probability. LoRa/LoRaWAN, like other frequency modulation based radio interfaces, exhibits the \textit{capture effect} in which only the stronger signal of colliding signals will be extracted. This leads to unfairness, where far nodes or nodes experiencing higher attenuation are less likely to see their packets received correctly. Therefore, we secondly develop a transmission power control algorithm to balance the received signal powers from all nodes regardless of their distances from the gateway for a fair data extraction. Simulations show that our approach achieves higher fairness in data rate than the state-of-art in almost all network configurations.

Connected through hyperedge HE-1 (induced by source paper "performance analysis of single cell adaptive data rate enabled lorawan"):
  with members:
    - "how agile is the adaptive data rate mechanism of lorawan"
    - "scalability analysis of a lora network under imperfect orthogonality"
    - "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"
    - "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control"

Connected through hyperedge HE-2 (induced by source paper "capture aware sequential waterfilling for lorawan adaptive data rate"):
  with members:
    - "how agile is the adaptive data rate mechanism of lorawan"
    - "scalability analysis of a lora network under imperfect orthogonality"
    - "on lorawan scalability empirical evaluation of susceptibility to inter network interference"
    - "low power wide area network analysis can lora scale"
    - "mitigating inter network interference in lora networks"

Connected through hyperedge HE-3 (induced by source paper "how agile is the adaptive data rate mechanism of lorawan"):
  with members:
    - "does bidirectional traffic do more harm than good in lorawan based lpwa networks"
    - "low power wide area networks an overview"

Connected through hyperedge HE-4 (induced by source paper "free fine grained scheduling for reliable and energy efficient data collection in lorawan"):
  with members:
    - "does bidirectional traffic do more harm than good in lorawan based lpwa networks"

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)