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 "connectivity of underlay cognitive radio networks with directional antennas"):
  Abstract: In underlay cognitive radio networks (CRNs), the connectivity of secondary users (SUs) is difficult to be guaranteed due to the existence of primary users. Most prior studies only consider cognitive radio networks equipped with omni-directional antennas causing  high interference  at SUs. We name such CRNs with omni-directional antennas as Omn-CRNs. Compared with an omni-directional antenna, a directional antenna can concentrate the transmitting/receiving capability at a certain direction, consequently resulting in the less interference. In this paper, we investigate the connectivity of SUs in CRNs with directional antennas (named as Dir-CRNs). In particular, we derive closed-form expressions of the connectivity of SUs of both Dir-CRNs and Omn-CRNs, thus enabling tractability. We show that the connectivity of SUs is mainly affected by two constraints: the  spectrum availability  of SUs and the  topological connectivity  of SUs. Extensive simulations validate the accuracy of our proposed models. Meanwhile, we also show that Dir-CRNs can have higher connectivity than Omn-CRNs mainly due to the lower interference, the higher spectrum availability, and the higher topological connectivity brought by directional antennas. Moreover, we also extend our analysis with considering transmission power efficiency. The simulation results show that Dir-CRNs require less transmission power to establish links than Omn-CRNs. We further investigate the throughput capacity of SUs, which is shown to heavily depend on the connectivity of SUs.

Includes members:
  Member "on the performance analysis of underlay cognitive radio systems a deployment perspective":
    also connected through other hyperedges:
      (none)
  Member "stochastic geometry modeling and performance evaluation of mmwave cellular communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coverage rate tradeoff in mmwave heterogeneous networks with generalized user association")
  Member "a geometrical based throughput bound analysis for device to device communications in cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "interference exploitation in full duplex communications trading interference power for both uplink and downlink power savings")
  Member "directional antennas improve the link connectivity of interference limited ad hoc networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spatial networks with wireless applications")
  Member "coverage and rate analysis for millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimizing information freshness in wireless networks a stochastic geometry approach")
  Member "wideband spectrum sensing for cognitive radio networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "20 years of evolution from cognitive to intelligent communications")
  Member "a tractable approach to coverage and rate in cellular 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.NI(Networking and Internet Architecture)