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 "random access analysis for massive iot networks under a new spatio temporal model a stochastic geometry approach"):
  Abstract: Massive Internet of Things (mIoT) has provided an auspicious opportunity to build powerful and ubiquitous connections that faces a plethora of new challenges, where cellular networks are potential solutions due to their high scalability, reliability, and efficiency. The Random Access CHannel (RACH) procedure is the first step of connection establishment between IoT devices and Base Stations (BSs) in the cellular-based mIoT network, where modelling the interactions between static properties of physical layer network and dynamic properties of queue evolving in each IoT device are challenging. To tackle this, we provide a novel traffic-aware spatio-temporal model to analyze RACH in cellular-based mIoT networks, where the physical layer network is modelled and analyzed based on stochastic geometry in the spatial domain, and the queue evolution is analyzed based on probability theory in the time domain. For performance evaluation, we derive the exact expressions for the preamble transmission success probabilities of a randomly chosen IoT device with different RACH schemes in each time slot, which offer insights into effectiveness of each RACH scheme. Our derived analytical results are verified by the realistic simulations capturing the evolution of packets in each IoT device. This mathematical model and analytical framework can be applied to evaluate the performance of other types of RACH schemes in the cellular-based networks by simply integrating its preamble transmission principle.

Includes members:
  Member "stochastic coalitional games for cooperative random access in m2m communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ran slicing for massive iot and bursty urllc service multiplexing analysis and optimization")
  Member "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "artificial noise aided secure transmission in large scale spectrum sharing networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "physical layer security in the finite blocklength regime over fading channels")
  Member "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "physical layer security in three tier wireless sensor networks a stochastic geometry approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and energy efficient transmissions in cache enabled heterogeneous cellular networks performance analysis and optimization")
  Member "on stochastic geometry modeling of cellular uplink transmission with truncated channel inversion power control":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a spatiotemporal framework for information freshness in iot uplink networks")
  Member "analytical modeling of uplink cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic tdd interference analysis in macro cell and small cell deployments")
  Member "internet of things iot a vision architectural elements and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "neuromemristive circuits for edge computing a review")

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)