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: "scalability analysis of a lora network under imperfect orthogonality"
  Abstract: Low-power wide-area network (LPWAN) technologies are gaining momentum for internet-of-things (IoT) applications since they promise wide coverage to a massive number of battery-operated devices using grant-free medium access. LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has emerged as the widely adopted LPWAN solution. By using chirp spread spectrum modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers coverage to wide-area applications while supporting high-density of devices. However, thus far its scalability performance has been inadequately modeled and the effect of interference resulting from the imperfect orthogonality of the SFs has not been considered. In this paper, we present an analytical model of a single-cell LoRa system that accounts for the impact of interference among transmissions over the same SF (co-SF) as well as different SFs (inter-SF). By modeling the interference field as Poisson point process under duty-cycled ALOHA, we derive the signal-to-interference ratio (SIR) distributions for several interference conditions. Results show that, for a duty cycle as low as 0.33%, the network performance under co-SF interference alone is considerably optimistic as the inclusion of inter-SF interference unveils a further drop in the success probability and the coverage probability of approximately 10% and 15%, respectively for 1500 devices in a LoRa channel. Finally, we illustrate how our analysis can characterize the critical device density with respect to cell size for a given reliability target.

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"
    - "fair adaptive data rate allocation and power control in lorawan"
    - "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 "lora beyond aloha an investigation of alternative random access protocols"):
  with members:
    - "analysis of lorawan uplink with multiple demodulating paths and capture effect"
    - "slotted aloha on lorawan design analysis and deployment"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "hybrid coded replication in lora networks"):
  with members:
    - "application layer coding for iot benefits limitations and implementation aspects"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "beyond 5g low power wide area networks a lorawan suitability study"):
  with members:
    - "hybrid coded replication in lora networks"
    - "low power wide area networks an overview"

Connected through hyperedge HE-5 (induced by source paper "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "machine learning in iot security current solutions and future challenges"
    - "toward intelligent vehicular networks a machine learning framework"
    - "distributed learning for channel allocation over a shared spectrum"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "deep learning for radio resource allocation in multi cell networks"

Connected through hyperedge HE-6 (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"
    - "lora throughput analysis with imperfect spreading factor orthogonality"
    - "low power wide area network analysis can lora scale"
    - "matching theory for future wireless networks fundamentals and applications"

Connected through hyperedge HE-7 (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"
    - "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"
    - "mitigating inter network interference in lora networks"

Connected through hyperedge HE-8 (induced by source paper "analysis of lorawan uplink with multiple demodulating paths and capture effect"):
  with members:
    - "lora throughput analysis with imperfect spreading factor orthogonality"
    - "analysis of latency and mac layer performance for class a lorawan"
    - "low power wide area network analysis can lora scale"

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.IT(Information Theory)