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: "large system analysis of power normalization techniques in massive mimo"
  Abstract: Linear precoding has been widely studied in the context of massive multiple-input multiple-output (MIMO) together with two common power normalization techniques, namely, matrix normalization (MN) and vector normalization (VN). Despite this, their effect on the performance of massive MIMO systems has not been thoroughly studied yet. The aim of this paper is to fulfill this gap by using large system analysis. Considering a system model that accounts for channel estimation, pilot contamination, arbitrary pathloss, and per-user channel correlation, we compute tight approximations for the signal-to-interference-plus-noise ratio and the rate of each user equipment in the system while employing maximum ratio transmission (MRT), zero forcing (ZF), and regularized ZF precoding under both MN and VN techniques. Such approximations are used to analytically reveal how the choice of power normalization affects the performance of MRT and ZF under uncorrelated fading channels. It turns out that ZF with VN resembles a sum rate maximizer while it provides a notion of fairness under MN. Numerical results are used to validate the accuracy of the asymptotic analysis and to show that in massive MIMO, noncoherent interference and noise, rather than pilot contamination, are often the major limiting factors of the considered precoding schemes.

Connected through hyperedge HE-1 (induced by source paper "large system analysis of massive mimo with optimal m mmse processing"):
  with members:
    - "covariance matrix estimation for massive mimo"
    - "fundamental asymptotic behavior of two user distributed massive mimo"
    - "a bilinear equalizer for massive mimo systems"
    - "covariance matrix estimation in massive mimo"
    - "massive mimo has unlimited capacity"
    - "massive mimo with imperfect channel covariance information"
    - "massive mimo with multi cell mmse processing exploiting all pilots for interference suppression"
    - "massive mimo in real propagation environments do all antennas contribute equally"

Connected through hyperedge HE-2 (induced by source paper "joint unicast and multi group multicast transmission in massive mimo systems"):
  with members:
    - "hybrid precoding for multi group physical layer multicasting"
    - "max min fair transmit precoding for multi group multicasting in massive mimo"
    - "multigroup multicast precoding in massive mimo"
    - "reducing the computational complexity of multicasting in large scale antenna systems"
    - "joint beamforming and broadcasting in massive mimo"
    - "hybrid precoding for physical layer multicasting"
    - "on the spectral efficiency and security enhancements of noma assisted multicast unicast streaming"
    - "massive mimo ten myths and one critical question"

Connected through hyperedge HE-3 (induced by source paper "max min fair transmit precoding for multi group multicasting in massive mimo"):
  with members:
    - "multigroup multicast precoding in massive mimo"
    - "reducing the computational complexity of multicasting in large scale antenna systems"
    - "joint beamforming and broadcasting in massive mimo"
    - "no downlink pilots are needed in tdd massive mimo"
    - "massive mimo ten myths and one critical question"
    - "multicast multigroup beamforming for per antenna power constrained large scale arrays"
    - "massive mimo performance evaluation based on measured propagation data"
    - "massive mimo multicasting in noncooperative cellular networks"

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)