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: "performance analysis of multi user massive mimo downlink under channel non reciprocity and imperfect csi"
  Abstract: This paper analyzes the performance of linearly precoded time division duplex based multi-user massive MIMO downlink system under joint impacts of channel non-reciprocity (NRC) and imperfect channel state information. We consider a generic and realistic NRC model that accounts for transceiver frequency-response as well as mutual coupling mismatches at both user equipment (UE) and base station (BS) sides. The analysis covers two most prominent forms of linear precoding schemes, namely, zero-forcing (ZF) and maximum-ratio transmission (MRT), and assumes that only the statistical properties of the beamformed channel are used at the UE side to decode the received signal. Under the approximation of i.i.d. Gaussian channels, closed-form analytical expressions are derived for the effective signal to interference and noise ratios (SINRs) and the corresponding capacity lower bounds. The expressions show that, in moderate to high SNR, the additional interference caused by imperfect NRC calibration can degrade the performance of both precoders significantly. Moreover, ZF is shown to be more sensitive to NRC than MRT. Numerical evaluations with practical NRC levels indicate that this performance loss in the spectral efficiency can be as high as 42% for ZF, whereas it is typically less than 13% for MRT. It is also shown that due to the NRC, the asymptotic large-antenna performance of both precoders saturate to an identical finite level. The derived analytical expressions provide useful tools and valuable technical insight, e.g., into calculating the NRC calibration requirements in BSs and UEs for any given specific performance targets in terms of effective SINR or the system capacity bound.

Connected through hyperedge HE-1 (induced by source paper "estimation and mitigation of channel non reciprocity in massive mimo"):
  with members:
    - "joint beamforming and broadcasting in massive mimo"
    - "analysis of massive mimo with hardware impairments and different channel models"
    - "massive mimo for next generation wireless systems"
    - "scalable synchronization and reciprocity calibration for distributed multiuser mimo"
    - "five disruptive technology directions for 5g"
    - "massive mu mimo downlink tdd systems with linear precoding and downlink pilots"
    - "channel estimation and linear precoding in multiuser multiple antenna tdd systems"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-2 (induced by source paper "robust transmission for massive mimo downlink with imperfect csi"):
  with members:
    - "on optimizing feedback interval for temporally correlated mimo channels with transmit beamforming and finite rate feedback"
    - "analysis and augmented spatial processing for uplink ofdma mu mimo receiver with transceiver i q imbalance and external interference"
    - "free deterministic equivalents for the analysis of mimo multiple access channel"
    - "pilot reuse for massive mimo transmission over spatially correlated rayleigh fading channels"
    - "multi user linear precoding for multi polarized massive mimo system under imperfect csit"
    - "asymptotic rate analysis of downlink multi user systems with co located and distributed antennas"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "statistical eigenmode transmission over jointly correlated mimo channels"

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)