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 "how much spectrum is too much in millimeter wave wireless access"):
  Abstract: Great increase in wireless access rates might be attainable using the large amount of spectrum available in the millimeter wave (mmWave) band. However, higher propagation losses inherent in these frequencies must be addressed, especially at ranges beyond 100 m and in non-line-of-sight (NLOS) settings. In contrast to the interference limited legacy cellular systems, where using more bandwidth is favorable, to use wider bandwidth for mmWave channels in noise limited settings may be ineffective or even counterproductive when accounting for channel estimation penalty. In this paper, we quantify the maximum beneficial bandwidth for mmWave transmission in some typical deployment scenarios where pilot-based channel estimation penalty is considered assuming a minimum mean square error channel estimator at the receiver. We find that, under I.I.D. block fading model with coherence time    $T_{c}$    and coherence bandwidth    $B_{c}$   , for transmitters and receivers equipped with a single antenna, the optimal (rate maximizing) signal-to-noise-ratio is a constant that only depends on the product    $B_{c}T_{c}$   , which measures the channel coherence and equals the average number of orthogonal symbols per each independent channel coefficient. That is, for fixed channel coherence    $B_{c}T_{c}$   , the optimal bandwidth scales linearly with the received signal power. Under 3GPP urban micro NLOS path loss model with coherence time    $T_{c}=5$    ms and coherence bandwidth    $B_{c}=10$    MHz, using 52-dBm equivalent isotropic radiated power (EIRP) at the transmitter and 11-dBi antenna gain at the receiver, the maximum beneficial bandwidth at 28 (resp. 39) GHz is less than 1 GHz at a distance beyond 210 (resp. 170) m with maximum throughput about 200 Mbps, and less than 100 MHz beyond 400 (resp. 310) m with maximum throughput about 20 Mbps. At EIRP of 85 dBm, corresponding to the FCC limit of 75 dBm per 100 MHz, 1 Gbps rate can be delivered using 1-GHz bandwidth up to 860 (resp. 680) m.

Includes members:
  Member "modeling and analyzing millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "coverage analysis of uavs in millimeter wave networks a stochastic geometry approach")
  Member "unified capacity limit of non coherent wideband fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "capacity scaling in a non coherent wideband massive simo block fading channel")
  Member "gbps user rates using mmwave relayed backhaul with high gain antennas":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "suburban fixed wireless access channel measurements and models at 28 ghz for 90 outdoor coverage")
  Member "millimeter wave channel modeling and cellular capacity evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coverage analysis of uavs in millimeter wave networks a stochastic geometry approach")
  Member "noncoherent capacity of underspread fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "interference cancellation and iterative detection for orthogonal time frequency space modulation")

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)