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 "finite alphabet precoding for massive mu mimo with low resolution dacs"):
  Abstract: Massive multiuser multiple-input multiple-output (MU-MIMO) systems are expected to be the core technology in fifth-generation wireless systems because they significantly improve spectral efficiency. However, the requirement for a large number of radio frequency (RF) chains results in high hardware costs and power consumption, which obstruct the commercial deployment of massive MIMO systems. A potential solution is to use low-resolution digital-to-analog converters (DAC)/analog-to-digital converters for each antenna and RF chain. However, using low-resolution DACs at the transmit side directly limits the degree of freedom of output signals and thus poses a challenge to the precoding design. In this study, we develop efficient and universal algorithms for a downlink massive MU-MIMO system with finite-alphabet precodings. Our algorithms are developed based on the alternating direction method of multipliers (ADMM) framework. The original ADMM does not converge in a nonlinear discrete optimization problem. The primary cause of this problem is that the alternating (update) directions in ADMM on one side are biased, and those on the other side are unbiased. By making the two updates consistent in an unbiased manner, we develop two algorithms: one demonstrates excellent performance and the other possesses a significantly low computational complexity. Compared with state-of-the-art techniques, the proposed precoding algorithms present significant advantages in performance and computational complexity.

Includes members:
  Member "energy efficiency of mmwave massive mimo precoding with low resolution dacs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mimo noma networks relying on reconfigurable intelligent surface a signal cancellation based design")
  Member "multipair massive mimo relaying systems with one bit adc s and dac s":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "supervised learning for multi hop mu mimo communications with one bit transceivers")
  Member "1 bit massive mu mimo precoding in vlsi":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral efficiency of one bit sigma delta massive mimo")
  Member "throughput analysis of massive mimo uplink with low resolution adcs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "channel estimation and performance analysis of one bit massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "performance analysis of quantized uplink massive mimo ofdm with oversampling under adjacent channel interference")
  Member "full duplex massive mimo relaying systems with low resolution adcs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "new equivalent model of quantizer with noisy input and its application for adc resolution determination in an uplink mimo receiver")
  Member "massive mu mimo ofdm downlink with one bit dacs and linear precoding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "training for channel estimation in nonlinear multi antenna transceivers")
  Member "bayes optimal joint channel and data estimation for massive mimo with low precision adcs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")

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)