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 "the new frontier in ran heterogeneity multi tier drone cells"):
  Abstract: In cellular networks, the locations of the radio access network (RAN) elements are determined mainly based on the long-term traffic behaviour. However, when the random and hard-to-predict spatio-temporal distribution of the traffic (load,demand) does not fully match the fixed locations of the RAN elements (supply), some performance degradation becomes inevitable. The concept of multi-tier cells (heterogeneous networks, HetNets) has been introduced in 4G networks to alleviate this mismatch. However, as the traffic distribution deviates more and more from the long-term average, even the HetNet architecture will have difficulty in coping up with the erratic supply-demand mismatch, unless the RAN is grossly over-engineered (which is a financially non-viable solution). In this article, we study the opportunistic utilization of low-altitude unmanned aerial platforms equipped with base stations (BSs), i.e., drone-BSs, in 5G networks. In particular, we envisage a multi-tier drone-cell network complementing the terrestrial HetNets. The variety of equipment, and non-rigid placement options allow utilizing multitier drone-cell networks to serve diversified demands. Hence, drone-cells bring the supply to where the demand is, which sets new frontiers for the heterogeneity in 5G networks. We investigate the advancements promised by drone-cells, and discuss the challenges associated with their operation and management. We propose a drone-cell management framework (DMF) benefiting from the synergy among software defined networking (SDN), network functions virtualization (NFV), and cloud-computing. We demonstrate DMF mechanisms via a case study, and numerically show that it can reduce the cost of utilizing drone-cells in multitenancy cellular networks.

Includes members:
  Member "on the number and 3d placement of drone base stations in wireless cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint 3d maneuver and power adaptation for secure uav communication with comp reception")
  Member "optical communication in space challenges and mitigation techniques":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unmanned aerial vehicles uavs a survey on civil applications and key research challenges")
  Member "designing and implementing future aerial communication networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "backhaul aware optimization of uav base station location and bandwidth allocation for profit maximization")
  Member "fronthaul constrained cloud radio access networks insights and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on the capacity regions of cloud radio access networks with limited orthogonal fronthaul")
  Member "hethetnets heterogeneous traffic distribution in heterogeneous wireless cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deployment of drone base stations for cellular communication without apriori user distribution information")
  Member "free space optical communication challenges and mitigation techniques":
    also connected through other hyperedges:
      (none)

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)