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: "mitigating inter network interference in lora networks"
  Abstract: Long Range (LoRa) is a popular technology used to construct Low-Power Wide-Area Network (LPWAN) networks. Given the popularity of LoRa it is likely that multiple independent LoRa networks are deployed in close proximity. In this situation, neighbouring networks interfere and methods have to be found to combat this interference. In this paper we investigate the use of directional antennae and the use of multiple base stations as methods of dealing with inter-network interference. Directional antennae increase signal strength at receivers without increasing transmission energy cost. Thus, the probability of successfully decoding the message in an interference situation is improved. Multiple base stations can alternatively be used to improve the probability of receiving a message in a noisy environment. We compare the effectiveness of these two approaches via simulation. Our findings show that both methods are able to improve LoRa network performance in interference settings. However, the results show that the use of multiple base stations clearly outperforms the use of directional antennae. For example, in a setting where data is collected from 600 nodes which are interfered by four networks with 600 nodes each, using three base stations improves the Data Extraction Rate (DER) from 0.24 to 0.56 while the use of directional antennae provides an increase to only 0.32.

Connected through hyperedge HE-1 (induced by source paper "tackling contention through cooperation a distributed federation in lorawan space"):
  with members:
    - "on lorawan scalability empirical evaluation of susceptibility to inter network interference"
    - "does bidirectional traffic do more harm than good in lorawan based lpwa networks"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

Connected through hyperedge HE-2 (induced by source paper "performance analysis of clustered lora networks"):
  with members:
    - "deep learning in physical layer communications"
    - "low power wide area networks for sustainable iot"
    - "low power wide area network analysis can lora scale"
    - "wirelessly powered backscatter communication networks modeling coverage and capacity"
    - "enhancing the physical layer security of non orthogonal multiple access in large scale networks"
    - "modeling and performance analysis of clustered device to device networks"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

Connected through hyperedge HE-3 (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"
    - "fair adaptive data rate allocation and power control in lorawan"
    - "on lorawan scalability empirical evaluation of susceptibility to inter network interference"
    - "low power wide area network analysis can lora scale"

Connected through hyperedge HE-4 (induced by source paper "on lorawan scalability empirical evaluation of susceptibility to inter network interference"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "understanding the iot connectivity landscape a contemporary m2m radio technology roadmap"

Connected through hyperedge HE-5 (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"
    - "understanding the limits of lorawan"
    - "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)