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 "linear precoding based on polynomial expansion reducing complexity in massive mimo"):
  Abstract: Large-scale multi-user multiple-input multiple-output (MIMO) techniques have the potential to bring tremendous improvements for future communication systems. Counter-intuitively, the practical issues of having uncertain channel knowledge, high propagation losses, and implementing optimal non-linear precoding are solved more-or-less automatically by enlarging system dimensions. However, the computational precoding complexity grows with the system dimensions. For example, the close-to-optimal regularized zero-forcing (RZF) precoding is very complicated to implement in practice, since it requires fast inversions of large matrices in every coherence period. Motivated by the high performance of RZF, we propose to replace the matrix inversion by a truncated polynomial expansion (TPE), thereby obtaining the new TPE precoding scheme which is more suitable for real-time hardware implementation. The degree of the matrix polynomial can be adapted to the available hardware resources and enables smooth transition between simple maximum ratio transmission (MRT) and more advanced RZF. #R##N#By deriving new random matrix results, we obtain a deterministic expression for the asymptotic signal-to-interference-and-noise ratio (SINR) achieved by TPE precoding in large-scale MIMO systems. Furthermore, we provide a closed-form expression for the polynomial coefficients that maximizes this SINR. To maintain a fixed per-user rate loss as compared to RZF, the polynomial degree does not need to scale with the system, but it should be increased with the quality of the channel knowledge and the signal-to-noise ratio (SNR).

Includes members:
  Member "linear precoding based on polynomial expansion large scale multi cell mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asymptotic analysis of rzf over double scattering channels with mmse estimation")
  Member "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "low complexity channel estimation in large scale mimo using polynomial expansion":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy based modulation for noncoherent massive simo systems")
  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 "pareto characterization of the multicell mimo performance region with simple receivers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "linear transceiver optimization in multicell mimo based on the generalized benders decomposition")
  Member "mimo interference alignment over correlated channels with imperfect csi":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the feasibility of interference alignment in compounded mimo broadcast channels with antenna correlation and mixed user classes")
  Member "a new approach for capacity analysis of large dimensional multi antenna channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "linear precoding based on polynomial expansion large scale multi cell mimo systems")

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)