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 "self organized scheduling request for uplink 5g networks a d2d clustering approach"):
  Abstract: In one of the several manifestations, the future cellular networks are required to accommodate a massive number of devices; several orders of magnitude compared to today's networks. At the same time, the future cellular networks will have to fulfill stringent latency constraints. To that end, one problem that is posed as a potential showstopper is extreme congestion for requesting uplink scheduling over the physical random access channel (PRACH). Indeed, such congestion drags along scheduling delay problems. In this paper, the use of self-organized device-to-device (D2D) clustering is advocated for mitigating PRACH congestion. To this end, the paper proposes two D2D clustering schemes, namely; Random-Based Clustering (RBC) and Channel-Gain-Based Clustering (CGBC). Accordingly, this paper sheds light on random access within the proposed D2D clustering schemes and presents a case study based on a stochastic geometry framework. For the sake of objective evaluation, the D2D clustering is benchmarked by the conventional scheduling request procedure. Accordingly, the paper offers insights into useful scenarios that minimize the scheduling delay for each clustering scheme. Finally, the paper discusses the implementation algorithm and some potential implementation issues and remedies.

Includes members:
  Member "power control and channel allocation for d2d underlaid cellular networks":
    also connected through other hyperedges:
      (none)
  Member "ultra reliable and low latency wireless communication tail risk and scale":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimized age of information tail for ultra reliable low latency communications in vehicular networks")
  Member "sinr and throughput of dense cellular networks with stretched exponential path loss":
    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 "modeling and analysis of cellular networks using stochastic geometry a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "area spectral efficiency and sinr scaling laws in multi antenna cellular networks")
  Member "the meta distribution of the sir for cellular networks with power control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a spatiotemporal framework for information freshness in iot uplink networks")
  Member "stochastic geometry modeling and analysis of single and multi cluster wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stochastic design and analysis of wireless cloud caching networks")
  Member "spatiotemporal stochastic modeling of iot enabled cellular networks scalability and stability analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "analytical modeling of interference aware power control for the uplink of heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a spatiotemporal model for peak aoi in uplink iot networks time vs event triggered traffic")

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)