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: "does bidirectional traffic do more harm than good in lorawan based lpwa networks"
  Abstract: The need for low power, long range and low cost connectivity to meet the requirements of IoT applications has led to the emergence of Low Power Wide Area (LPWA) networking technologies. The promise of these technologies to wirelessly connect massive numbers of geographically dispersed devices at a low cost continues to attract a great deal of attention in the academic and commercial communities. Several rollouts are already underway even though the performance of these technologies is yet to be fully understood. In light of these developments, tools to carry out `what-if analyses' and pre-deployment studies are needed to understand the implications of choices that are made at design time. While there are several promising technologies in the LPWA space, this paper specifically focuses on the LoRa/LoRaWAN technology. In particular, we present LoRaWANSim, a simulator which extends the LoRaSim tool to add support for the LoRaWAN MAC protocol, which employs bidirectional communication. This is a salient feature not available in any other LoRa simulator. Subsequently, we provide vital insights into the performance of LoRaWAN based networks through extensive simulations. In particular, we show that the achievable network capacity reported in earlier studies is quite optimistic. The introduction of downlink traffic can have a significant impact on the uplink throughput. The number of transmit attempts recommended in the LoRaWAN specification may not always be the best choice. We also highlight the energy consumption versus reliability trade-offs associated with the choice of number of retransmission attempts.

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"
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"
    - "mitigating inter network interference in lora networks"

Connected through hyperedge HE-2 (induced by source paper "a thorough study of lorawan performance under different parameter settings"):
  with members:
    - "employing p csma on a lora network simulator"
    - "uncoordinated access schemes for the iot approaches regulations and performance"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "a complete lorawan model for single gateway scenarios"):
  with members:
    - "a thorough study of lorawan performance under different parameter settings"
    - "analysis of latency and mac layer performance for class a lorawan"
    - "scalability analysis of large scale lorawan networks in ns 3"
    - "low power wide area network analysis can lora scale"

Connected through hyperedge HE-4 (induced by source paper "how agile is the adaptive data rate mechanism of lorawan"):
  with members:
    - "fair adaptive data rate allocation and power control in lorawan"
    - "low power wide area networks an overview"

Connected through hyperedge HE-5 (induced by source paper "free fine grained scheduling for reliable and energy efficient data collection in lorawan"):
  with members:
    - "fair adaptive data rate allocation and power control in lorawan"

Connected through hyperedge HE-6 (induced by source paper "aggressive fragmentation strategy for enhanced network performance in dense lpwans"):
  with members:
    - "an analysis of packet fragmentation impact in lpwan"

Connected through hyperedge HE-7 (induced by source paper "selective jamming of lorawan using commodity hardware"):
  with members:
    - "on lorawan scalability empirical evaluation of susceptibility to inter network interference"
    - "low power wide area network analysis can lora scale"

Connected through hyperedge HE-8 (induced by source paper "scalability analysis of large scale lorawan networks in ns 3"):
  with members:
    - "low power wide area network analysis can lora scale"
    - "low power wide area networks an overview"

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)