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 "rapid fading due to human blockage in pedestrian crowds at 5g millimeter wave frequencies"):
  Abstract: Rapidly fading channels caused by pedestrians in dense urban environments will have a significant impact on millimeter-wave (mmWave) communications systems that employ electrically-steerable and narrow beamwidth antenna arrays. A peer-to-peer (P2P) measurement campaign was conducted with 7-degree, 15-degree, and 60-degree half-power beamwidth (HPBW) antenna pairs at 73.5 GHz and with 1 GHz of RF null-to-null bandwidth in a heavily populated open square scenario in Brooklyn, New York, to study blockage events caused by typical pedestrian traffic. Antenna beamwidths that range approximately an order of magnitude were selected to gain knowledge of fading events for antennas with different beamwidths since antenna patterns for mmWave systems will be electronically-adjustable. Two simple modeling approaches in the literature are introduced to characterize the blockage events by either a two-state Markov model or a four-state piecewise linear modeling approach. Transition probability rates are determined from the measurements and it is shown that average fade durations with a -5 dB threshold are 299.0 ms for 7-degree HPBW antennas and 260.2 ms for 60-degree HPBW antennas. The four-state piecewise linear modeling approach shows that signal strength decay and rise times are asymmetric for blockage events and that mean signal attenuations (average fade depths) are inversely proportional to antenna HPBW, where 7-degree and 60-degree HPBW antennas resulted in mean signal fades of 15.8 dB and 11.5 dB, respectively. The models presented herein are valuable for extending statistical channel models at mmWave to accurately simulate real-world pedestrian blockage events when designing fifth-generation (5G) wireless systems.

Includes members:
  Member "millimeter wave small scale spatial statistics in an urban microcell scenario":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "millimeter wave extended nyusim channel model for spatial consistency")
  Member "a novel millimeter wave channel simulator and applications for 5g wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the disruptions of 5g on data driven technologies and applications")
  Member "millimeter wave human blockage at 73 ghz with a simple double knife edge diffraction model and extension for directional antennas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "statistical blockage modeling and robustness of beamforming in millimeter wave systems")
  Member "transport layer performance in 5g mmwave cellular":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on recent advances in transport layer protocols")
  Member "investigation of prediction accuracy sensitivity and parameter stability of large scale propagation path loss models for 5g wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a millimeter wave channel simulator nyusim with spatial consistency and human blockage")
  Member "five disruptive technology directions for 5g":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")

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)