human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "spectrum sharing for massive access in ultra narrowband iot systems"):
  Abstract: Ultra-narrowband communications has become a signature feature for many emerging low-power wide-area (LPWA) networks. Specifically, using extremely narrowband signals helps the network connect more Internet-of-things (IoT) devices within a given band. It also improves robustness to interference, extending the coverage of the network. In this paper, we study the coexistence capability of UNB networks and their scalability to enable massive access. To this end, we develop a stochastic geometry framework to analyze and model UNB networks on a large scale. The framework captures the unique characteristics of UNB communications, including the asynchronous time-frequency access, signal repetition, and the absence of base station (BS) association. Closed-form expressions of the transmission success probability and network connection density are presented for several UNB protocols. We further discuss multiband access for UNB networks, proposing a low-complexity protocol. Our analysis reveals several insights on the geographical diversity achieved when devices do not connect to a single BS, the optimal number of signal repetitions, and how to utilize multiple bands without increasing the complexity of BSs. Simulation results are provided to validate the analysis, and they show that UNB communications enables a single BS to connect thousands of devices even in the presence of interfering networks.

Includes members:
  Member "sparse signal processing for grant free massive connectivity a future paradigm for random access protocols in the internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse optimization for green edge ai inference")
  Member "modeling and analysis of cellular networks using stochastic geometry a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "aggregation and resource scheduling in machine type communication networks a stochastic geometry approach":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatio temporal modeling for massive and sporadic access")
  Member "low power wide area networks an overview":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "random access based reliable uplink communication and power transfer using dynamic power splitting")
  Member "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance analysis of single cell adaptive data rate enabled lorawan")
  Member "transmission capacity of wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")

Question: Please tell me which class the source paper 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)