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: "linear precoding based on polynomial expansion large scale multi cell mimo systems"
  Abstract: Large-scale MIMO systems can yield a substantial improvements in spectral efficiency for future communication systems. Due to the finer spatial resolution and array gain achieved by a massive number of antennas at the base station, these systems have shown to be robust to inter-user interference and the use of linear precoding appears to be asymptotically optimal. However, from a practical point of view, most precoding schemes exhibit prohibitively high computational complexity as the system dimensions increase. For example, the near-optimal regularized zero forcing (RZF) precoding requires the inversion of a large matrix. To solve this issue, we propose in this paper to approximate the matrix inverse by a truncated polynomial expansion (TPE), where the polynomial coefficients are optimized to maximize the system performance. This technique has been recently applied in single cell scenarios and it was shown that a small number of coefficients is sufficient to reach performance similar to that of RZF, while it was not possible to surpass RZF. In a realistic multi-cell scenario involving large-scale multi-user MIMO systems, the optimization of RZF precoding has, thus far, not been feasible. This is mainly attributed to the high complexity of the scenario and the non-linear impact of the necessary regularizing parameters. On the other hand, the scalar coefficients in TPE precoding give hope for possible throughput optimization. To this end, we exploit random matrix theory to derive a deterministic expression of the asymptotic signal-to-interference-and-noise ratio for each user based on channel statistics. We also provide an optimization algorithm to approximate the coefficients that maximize the network-wide weighted max-min fairness. The optimization weights can be used to mimic the user throughput distribution of RZF precoding. Using simulations, we compare the network throughput of the proposed TPE precoding with that of the suboptimal RZF scheme and show that our scheme can achieve higher throughput using a TPE order of only 5.

Connected through hyperedge HE-1 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation"):
  with members:
    - "massive mimo has unlimited capacity"
    - "multi cell massive mimo performance with double scattering channels"
    - "no downlink pilots are needed in tdd massive mimo"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "achieving massive mimo spectral efficiency with a not so large number of antennas"
    - "random beamforming over quasi static and fading channels a deterministic equivalent approach"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "asymptotic analysis of double scattering channels"

Connected through hyperedge HE-2 (induced by source paper "channel correlation diversity in mu mimo systems analysis and measurements"):
  with members:
    - "two layer decoding in cellular massive mimo systems with spatial channel correlation"
    - "large scale fading decoding in cellular massive mimo systems with spatially correlated channels"
    - "uplink massive mimo for channels with spatial correlation"
    - "capacity scaling of massive mimo in strong spatial correlation regimes"
    - "massive mimo has unlimited capacity"
    - "massive mimo performance evaluation based on measured propagation data"
    - "on the matrix inversion approximation based on neumann series in massive mimo systems"
    - "power scaling of uplink massive mimo systems with arbitrary rank channel means"

Connected through hyperedge HE-3 (induced by source paper "elevation beamforming with full dimension mimo architectures in 5g systems a tutorial"):
  with members:
    - "asymptotic analysis of rzf over double scattering channels with mmse estimation"
    - "distributed fd mimo cellular evolution for 5g and beyond"
    - "overview of full dimension mimo in lte advanced pro"
    - "an effective limited feedback scheme for fd mimo based on noncoherent detection and kronecker product codebook"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "3d channel model in 3gpp"
    - "3d massive mimo systems modeling and performance analysis"
    - "a generalized spatial correlation model for 3d mimo channels based on the fourier coefficients of power spectrums"

Connected through hyperedge HE-4 (induced by source paper "multi band covariance interpolation with applications in massive mimo"):
  with members:
    - "compressive estimation of a stochastic process with unknown autocorrelation function"
    - "low complexity and statistically robust beamformer design for massive mimo systems"
    - "efficient downlink channel probing and uplink feedback in fdd massive mimo systems"
    - "truncated polynomial expansion downlink precoders and uplink detectors for massive mimo"
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "channel vector subspace estimation from low dimensional projections"
    - "mimo downlink scheduling with non perfect channel state knowledge"

Connected through hyperedge HE-5 (induced by source paper "cell edge aware precoding for downlink massive mimo cellular networks"):
  with members:
    - "distributed optimization of hierarchical small cell networks a gnep framework"
    - "massive mimo ten myths and one critical question"
    - "operating massive mimo in unlicensed bands for enhanced coexistence and spatial reuse"
    - "energy efficient design of mimo heterogeneous networks with wireless backhaul"
    - "a stochastic geometry framework for analyzing pairwise cooperative cellular networks"
    - "three practical aspects of massive mimo intermittent user activity pilot synchronism and asymmetric deployment"
    - "fractional pilot reuse in massive mimo systems"
    - "a tractable model for noncoherent joint transmission base station cooperation"

Connected through hyperedge HE-6 (induced by source paper "large system analysis of power normalization techniques in massive mimo"):
  with members:
    - "massive mimo has unlimited capacity"
    - "reducing the computational complexity of multicasting in large scale antenna systems"
    - "energy efficient downlink transmission for multicell massive das with pilot contamination"
    - "joint power allocation and user association optimization for massive mimo systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "pilot contamination is not a fundamental asymptotic limitation in massive mimo"
    - "massive mimo ten myths and one critical question"
    - "performance analysis of massive mimo for cell boundary users"

Connected through hyperedge HE-7 (induced by source paper "massive mimo pilot decontamination and channel interpolation via wideband sparse channel estimation"):
  with members:
    - "low complexity massive mimo subspace estimation and tracking from low dimensional projections"
    - "robust pilot decontamination based on joint angle and power domain discrimination"
    - "channel acquisition for massive mimo ofdm with adjustable phase shift pilots"
    - "rrh based massive mimo with on the fly pilot contamination control"
    - "pilot contamination is not a fundamental asymptotic limitation in massive mimo"
    - "cell free massive mimo uniformly great service for everyone"
    - "massive mimo for next generation wireless systems"
    - "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics"

Connected through hyperedge HE-8 (induced by source paper "max min sinr in large scale single cell mu mimo asymptotic analysis and low complexity transceivers"):
  with members:
    - "large system analysis of the energy consumption distribution in multi user mimo systems with mobility"
    - "large system analysis of base station cooperation for power minimization"
    - "optimal multiuser transmit beamforming a difficult problem with a simple solution structure"
    - "massive mimo for next generation wireless systems"
    - "joint beamforming and power control in coordinated multicell max min duality effective network and large system transition"
    - "scaling up mimo opportunities and challenges with very large arrays"
    - "large system analysis of linear precoding in correlated miso broadcast channels under limited feedback"
    - "robust monotonic optimization framework for multicell miso 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)