human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "on the ergodic capacity of mimo free space optical systems over turbulence channels"
  Abstract: Free-space optical (FSO) communications can achieve high capacity with huge unlicensed optical spectrum and low operational costs. The corresponding performance analysis of FSO systems over turbulence channels is very limited, particularly when using multiple apertures at both transmitter and receiver sides. This paper aims to provide the ergodic capacity characterization of multiple-input–multiple-output (MIMO) FSO systems over atmospheric turbulence-induced fading channels. The fluctuations of the irradiance of optical channels distorted by atmospheric conditions is usually described by a gamma–gamma    $(\Gamma\Gamma)$   distribution, and the distribution of the sum of    $\Gamma\Gamma$   random variables (RVs) is required to model the MIMO optical links. We use an    $\alpha-\mu$   distribution to efficiently approximate the probability density function (pdf) of the sum of independent and identical distributed    $\Gamma\Gamma$   RVs through moment-based estimators. Furthermore, the pdf of the sum of independent, but not necessarily identically distributed    $\Gamma\Gamma$   RVs can be efficiently approximated by a finite weighted sum of pdfs of    $\Gamma\Gamma$   distributions. Based on these reliable approximations, novel and precise analytical expressions for the ergodic capacity of MIMO FSO systems are derived. Additionally, we deduce the asymptotic simple expressions in high signal-to-noise ratio regimes, which provide useful insights into the impact of the system parameters on the ergodic capacity. Finally, our proposed results are validated via Monte Carlo simulations.

Connected through hyperedge HE-1 (induced by source paper "distribution of the sum of fisher snedecor mathcal f random variables and its applications"):
  with members:
    - "on the statistics of the ratio of non constrained arbitrary alpha mu random variables a general framework and applications"
    - "rician k factor based analysis of xlos service probability in 5g outdoor ultra dense networks"
    - "on the multivariate gamma gamma distribution with arbitrary correlation and applications in wireless communications"
    - "analysis of energy efficiency in fading channels under qos constraints"

Connected through hyperedge HE-2 (induced by source paper "achievable rate of rician large scale mimo channels with transceiver hardware impairments"):
  with members:
    - "asymptotic analysis of su mimo channels with transmitter noise and mismatched joint decoding"
    - "linear massive mimo precoders in the presence of phase noise a large scale analysis"
    - "uplink performance of time reversal mrc in massive mimo systems subject to phase noise"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"
    - "massive mimo systems with non ideal hardware energy efficiency estimation and capacity limits"
    - "on the mimo capacity with residual transceiver hardware impairments"
    - "capacity limits and multiplexing gains of mimo channels with transceiver impairments"

Connected through hyperedge HE-3 (induced by source paper "on the multivariate gamma gamma distribution with arbitrary correlation and applications in wireless communications"):
  with members:
    - "unified performance analysis of mixed radio frequency free space optical dual hop transmission systems"
    - "statistical characterization of kappa mu shadowed fading"
    - "on the distribution of the sum of gamma gamma variates and applications in rf and optical wireless communications"

Question: Please tell me which class the center node 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)