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 "training for channel estimation in nonlinear multi antenna transceivers"):
  Abstract: Recent efforts to obtain high data rates in wireless systems have focused on what can be achieved in systems that have nonlinear or coarsely quantized transceiver architectures. Estimating the channel in such a system is challenging because the nonlinearities distort the channel estimation process. It is therefore of interest to determine how much training is needed to estimate the channel sufficiently well so that the channel estimate can be used during data communication. We provide a way to determine how much training is needed by deriving a lower bound on the achievable rate in a training-based scheme that can be computed and analyzed even when the number of antennas is very large. This lower bound can be tight, especially at high SNR. One conclusion is that the optimal number of training symbols may paradoxically be smaller than the number of transmitters for systems with coarsely-quantized transceivers. We show how the training time can be strongly dependent on the number of receivers, and give an example where doubling the number of receivers reduces the training time by about 37 percent.

Includes members:
  Member "beamforming with multiple one bit wireless transceivers":
    also connected through other hyperedges:
      (none)
  Member "capacity of multiple one bit transceivers in a rayleigh environment":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the capacity of miso channels with one bit adcs and dacs")
  Member "channel estimation and performance analysis of one bit massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "performance analysis of quantized uplink massive mimo ofdm with oversampling under adjacent channel interference")
  Member "uplink performance of wideband massive mimo with one bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "massive mu mimo ofdm downlink with one bit dacs and linear precoding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reconsidering linear transmit signal processing in 1 bit quantized multi user miso systems")
  Member "how much training is needed in one bit massive mimo systems at low snr":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "full duplex massive mimo relaying systems with low resolution adcs")
  Member "bayes optimal joint channel and data estimation for massive mimo with low precision adcs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "downlink achievable rate analysis in massive mimo systems with one bit dacs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)