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 "neural network optimized 1 bit precoding for massive mu mimo"):
  Abstract: Base station (BS) architectures for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems are equipped with hundreds of antennas to serve tens of users on the same time-frequency channel. The immense number of BS antennas incurs high system costs, power, and interconnect bandwidth. To circumvent these obstacles, sophisticated MU precoding algorithms that enable the use of 1-bit DACs have been proposed. Many of these precoders feature parameters that are, traditionally, tuned manually to optimize their performance. We propose to use deep-learning tools to automatically tune such 1-bit precoders. Specifically, we optimize the biConvex 1-bit PrecOding (C2PO) algorithm using neural networks. Compared to the original C2PO algorithm, our neural-network optimized (NNO-)C2PO achieves the same error-rate performance at $\bf 2\boldsymbol\times$ lower complexity. Moreover, by training NNO-C2PO for different channel models, we show that 1-bit precoding can be made robust to vastly changing propagation conditions.

Includes members:
  Member "a deep learning framework for optimization of miso downlink beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning enabled optimization of downlink beamforming under per antenna power constraints algorithms and experimental demonstration")
  Member "multilevel mimo detection with deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning to search for mimo detection")
  Member "one bit massive mimo precoding via a minimum symbol error probability design":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "massive mu mimo ofdm uplink with direct rf sampling and 1 bit adcs")
  Member "quantized constant envelope precoding with psk and qam signaling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral efficiency of one bit sigma delta massive mimo")
  Member "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "a model driven deep learning network for mimo detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "amp inspired deep networks for sparse linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "1 bit massive mu mimo precoding in vlsi":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spectral efficiency of one bit sigma delta massive mimo")

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)