human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "near optimal linear precoding with low complexity for massive mimo"):
  Abstract: Linear precoding techniques can achieve near- optimal capacity due to the special channel property in down- link massive MIMO systems, but involve high complexity since complicated matrix inversion of large size is required. In this paper, we propose a low-complexity linear precoding scheme based on the Gauss-Seidel (GS) method. The proposed scheme can achieve the capacity-approaching performance of the classical linear precoding schemes in an iterative way without complicated matrix inversion, which can reduce the overall complexity by one order of magnitude. The performance guarantee of the proposed GS-based precoding is analyzed from the following three aspects. At first, we prove that GS-based precoding satisfies the transmit power constraint. Then, we prove that GS-based precoding enjoys a faster convergence rate than the recently proposed Neumann-based precoding. At last, the convergence rate achieved by GS-based precoding is quantified, which reveals that GS-based precoding converges faster with the increasing number of BS antennas. To further accelerate the convergence rate and reduce the complexity, we propose a zone-based initial solution to GS-based precoding, which is much closer to the final solution than the traditional initial solution. Simulation results demonstrate that the proposed scheme outperforms Neumann- based precoding, and achieves the exact capacity-approaching performance of the classical linear precoding schemes with only a small number of iterations both in Rayleigh fading channels and spatially correlated channels.

Includes members:
  Member "large scale mimo detection for 3gpp lte algorithms and fpga implementations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized equalization with feedforward architectures for massive mu mimo")
  Member "linear precoding based on polynomial expansion large scale multi cell mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation")
  Member "five disruptive technology directions for 5g":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")
  Member "scaling up mimo opportunities and challenges with very large arrays":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "energy and spectral efficiency of very large multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "sum rates rate allocation and user scheduling for multi user mimo vector perturbation precoding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the throughput of large but finite mimo networks using schedulers")
  Member "mimo detection for high order qam based on a gaussian tree approximation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi user detection based on expectation propagation for the non coherent simo multiple access channel")

Question: Please tell me which class the source paper 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)