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: "massive mimo performance with imperfect channel reciprocity and channel estimation error"
  Abstract: Channel reciprocity in time-division duplexing (TDD) massive multiple-input multiple-output (MIMO) systems can be exploited to reduce the overhead required for the acquisition of channel state information (CSI). However, perfect reciprocity is unrealistic in practical systems due to random radio-frequency (RF) circuit mismatches in uplink and downlink channels. This can result in a significant degradation in the performance of linear precoding schemes, which are sensitive to the accuracy of the CSI. In this paper, we model and analyse the impact of RF mismatches on the performance of linear precoding in a TDD multi-user massive MIMO system, by taking the channel estimation error into considerations. We use the truncated Gaussian distribution to model the RF mismatch, and derive closed-form expressions of the output signal-to-interference-plus-noise ratio for maximum ratio transmission and zero forcing precoders. We further investigate the asymptotic performance of the derived expressions, to provide valuable insights into the practical system designs, including useful guidelines for the selection of the effective precoding schemes. Simulation results are presented to demonstrate the validity and accuracy of the proposed analytical results.

Connected through hyperedge HE-1 (induced by source paper "achieving phase coherency and gain stability in active antenna arrays for sub 6 ghz fdd and tdd fd mimo challenges and solutions"):
  with members:
    - "channel reciprocity calibration in tdd hybrid beamforming massive mimo systems"
    - "a framework for over the air reciprocity calibration for tdd massive mimo systems"
    - "toward a realistic assessment of multiple antenna hcns residual additive transceiver hardware impairments and channel aging"
    - "overview of full dimension mimo in lte advanced pro"
    - "reciprocity calibration for massive mimo proposal modeling and validation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "beyond 5g leveraging cell free tdd massive mimo using cascaded deep learning"):
  with members:
    - "an introduction to deep learning for the physical layer"
    - "a framework for over the air reciprocity calibration for tdd massive mimo systems"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "cell free massive mimo versus small cells"
    - "reciprocity calibration for massive mimo proposal modeling and validation"

Connected through hyperedge HE-3 (induced by source paper "wireless access in ultra reliable low latency communication urllc"):
  with members:
    - "how reliable and capable is multi connectivity"
    - "wireless channel dynamics and robustness for ultra reliable low latency communications"
    - "improving network availability of ultra reliable and low latency communications with multi connectivity"
    - "packet duplication in dual connectivity enabled 5g wireless networks overview and challenges"
    - "massive mimo for ultra reliable communications with constellations for dual coherent noncoherent detection"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "energy latency tradeoff in ultra reliable low latency communication with retransmissions"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"

Connected through hyperedge HE-4 (induced by source paper "on channel reciprocity to activate uplink channel training for downlink data transmission"):
  with members:
    - "short packet communications over multiple antenna rayleigh fading channels"
    - "what will 5g be"
    - "ultra reliable communication in 5g wireless systems"
    - "mimo interference alignment over correlated channels with imperfect csi"
    - "pilot contamination and precoding in multi cell tdd systems"

Connected through hyperedge HE-5 (induced by source paper "on channel reciprocity to activate uplink channel training for downlink wireless transmission in tactile internet applications"):
  with members:
    - "short packet communications over multiple antenna rayleigh fading channels"
    - "ultra reliable communication in 5g wireless systems"
    - "mimo interference alignment over correlated channels with imperfect csi"
    - "pilot contamination and precoding in multi cell tdd systems"

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)