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 "exploiting device to device communications to enhance spatial reuse for popular content downloading in directional mmwave small cells"):
  Abstract: With the explosive growth of mobile demand, small cells in millimeter-wave (mmWave) bands underlying macrocell networks have attracted intense interest from both academia and industry. MmWave communications in the 60-GHz band are able to utilize the huge unlicensed bandwidth to provide multiple Gb/s transmission rates. In this case, device-to-device (D2D) communications in mmWave bands should be fully exploited because there is no interference with the macrocell networks and higher achievable transmission rates. In addition, because there is less interference by directional transmission, multiple links including D2D links can be scheduled for concurrent transmissions (spatial reuse). With the popularity of content-based mobile applications, popular content downloading in the small cells needs to be optimized to improve network performance and enhance user experience. In this paper, we develop an efficient scheduling scheme for popular content downloading in mmWave small cells called popular content downloading scheduling (PCDS), where both D2D communications in close proximity and concurrent transmissions are exploited to improve transmission efficiency. In PCDS, a transmission path selection algorithm is designed to establish multihop transmission paths for users, aiming at better utilization of D2D communications and concurrent transmissions. After transmission path selection, a concurrent transmission scheduling algorithm is designed to maximize the spatial reuse gain. Through extensive simulations under various traffic patterns, we demonstrate that PCDS achieves near-optimal performance in terms of delay and throughput, as well as superior performance, compared with other existing protocols, particularly under heavy load. The impact of the maximum number of hops of transmission paths on its performance is also analyzed for a better understanding of the role of D2D communications.

Includes members:
  Member "exploiting multi hop relaying to overcome blockage in directional mmwave small cells":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relay assisted and qos aware scheduling to overcome blockage in mmwave backhaul networks")
  Member "exploiting device to device communications in joint scheduling of access and backhaul for mmwave small cells":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sub channel allocation for device to device underlaying full duplex mmwave small cells using coalition formation games")
  Member "iterative joint beamforming training with constant amplitude phased arrays in millimeter wave communications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "channel estimation and transmission for intelligent reflecting surface assisted thz communications")
  Member "modeling analysis and optimization of multicast device to device transmissions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "d2d multicast in underlay cellular networks with exclusion zones")
  Member "wireless backhaul node placement for small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "qos aware full duplex concurrent scheduling for millimeter wave wireless backhaul networks")
  Member "coding the beams improving beamforming training in mmwave communication system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sub channel allocation for device to device underlaying full duplex mmwave small cells using coalition formation games")
  Member "femtocell networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust resource allocation for pd noma based miso heterogeneous networks with comp technology")

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)