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 "towards 1 gbps ue in cellular systems understanding ultra dense small cell deployments"):
  Abstract: Today's heterogeneous networks comprised of mostly macrocells and indoor small cells will not be able to meet the upcoming traffic demands. Indeed, it is forecasted that at least a    $100\times$   network capacity increase will be required to meet the traffic demands in 2020. As a result, vendors and operators are now looking at using every tool at hand to improve network capacity. In this epic campaign, three paradigms are noteworthy, i.e., network densification, the use of higher frequency bands and spectral efficiency enhancement techniques. This paper aims at bringing further common understanding and analysing the potential gains and limitations of these three paradigms, together with the impact of idle mode capabilities at the small cells as well as the user equipment density and distribution in outdoor scenarios. Special attention is paid to network densification and its implications when transiting to ultra-dense small cell deployments. Simulation results show that comparing to the baseline case with an average inter site distance of 200 m and a 100 MHz bandwidth, network densification with an average inter site distance of 35 m can increase the average UE throughput by    $7.56\times$  , while the use of the 10 GHz band with a 500 MHz bandwidth can further increase the network capacity up to    $5\times$  , resulting in an average of 1.27 Gbps per UE. The use of beamforming with up to 4 antennas per small cell BS lacks behind with average throughput gains around 30% and cell-edge throughput gains of up to    $2\times$  . Considering an extreme densification, an average inter site distance of 5 m can increase the average and cell-edge UE throughput by    $18\times$   and    $48\times$  , respectively. Our study also shows how network densification reduces multi-user diversity, and thus proportional fair alike schedulers start losing their advantages with respect to round robin ones. The energy efficiency of these ultra-dense small cell deployments is also analysed, indicating the benefits of energy harvesting approaches to make these deployments more energy-efficient. Finally, the top ten challenges to be addressed to bring ultra-dense small cell deployments to reality are also discussed.

Includes members:
  Member "performance impact of los and nlos transmissions in small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coverage in downlink heterogeneous mmwave cellular networks with user centric small cell deployment")
  Member "study on scheduling techniques for ultra dense small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of uav communications in cell free massive mimo systems")
  Member "algorithms for enhanced inter cell interference coordination eicic in lte hetnets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analog subspace coding a new approach to coding for non coherent wireless networks")
  Member "capacity analysis of lte advanced hetnets with reduced power subframes and range expansion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "heuristic approach for jointly optimizing feicic and uav locations in multi tier lte advanced public safety hetnet")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint power control and lsfd for wireless powered cell free massive mimo")
  Member "will the area spectral efficiency monotonically grow as small cells go dense":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modeling and performance analysis in cache enabled millimeter wave hetnets with access and backhaul integration")
  Member "on the impact of hardware impairments on massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hardware distortion correlation has negligible impact on ul massive mimo spectral efficiency")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")

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)