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: "employing p csma on a lora network simulator"
  Abstract: Low-Power Wide-Area Networks (LPWANs) emerged to cover the needs of Internet of Things (IoT)-devices for operational longevity and long operating range. Among LPWANs, Long Range (LoRa) WAN has been the most promising; an upcoming IoT protocol, already adopted by big mobile operators like KPN and TTN. With LoRaWANs, IoT-devices transmit data to their corresponding gateways over many kilometers in a single hop and with 1% duty-cycle. However, in a LoRa network, any device claims the channel for data-transmission without performing channel-sensing or synchronization with other devices. This increases humongously the number of collisions of information-packets when the number of IoT-devices that are connected per gateway increases. #R##N#To improve the utilization of the channel, we propose the application of persistent-Carrier Sense Multiple Access (p-CSMA) protocols on the MAC layer of LoRaWANs. In this manuscript, we report on the initial design of a p-CSMA component for the simulation of LoRa networks in ns3. In particular, the classes adding p-CSMA functionality to the IoT-devices are presented. Additionally, the dependencies and relations between these classes and an existing LoRaWAN module on which they apply are detailed. Further, we evaluate this new p-CSMA LoRaWAN module in terms of Packet Reception Ratio (PRR) by simulating LoRa networks. The current report is the first step in the creation of a holistic p-CSMA module, directed to support network-researchers and connoisseurs in simulating all aspects of LoRa networks in ns3.

Connected through hyperedge HE-1 (induced by source paper "a thorough study of lorawan performance under different parameter settings"):
  with members:
    - "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"
    - "scalability analysis of large scale lorawan networks in ns 3"
    - "m2m massive access in lte rach performance evaluation in a smart city scenario"
    - "internet of things applications and challenges in technology and standardization"

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)