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 "supervised learning for multi hop mu mimo communications with one bit transceivers"):
  Abstract: This paper considers a nonlinear multi-hop multi-user multiple-input multiple-output (MU-MIMO) relay channel, in which multiple users send information symbols to a multi-antenna base station (BS) with one-bit analog-to-digital converters via intermediate relays, each with one-bit transceiver. To understand the fundamental limit of the detection performance, the optimal maximum-likelihood (ML) detector is proposed with the assumption of perfect and global channel state information (CSI) at the BS. This multi-user detector, however, is not practical due to the unrealistic CSI assumption and the overwhelming detection complexity. These limitations are addressed by presenting a novel detection framework inspired by supervised-learning. The key idea is to model the complicated multihop MU-MIMO channel as a simplified channel with much fewer and learnable parameters. One major finding is that, even using the simplified channel model, a near ML detection performance is achievable with a reasonable amount of pilot overheads in a certain condition. In addition, an online supervised-learning detector is proposed, which adaptively tracks channel variations. The idea is to update the model parameters with a reliably detected data symbol by treating it as a new training (labelled) data. Lastly, a multi-user detector using a deep neural network is proposed. Unlike the model-based approaches, this model-free approach enables to remove the errors in the simplified channel model, while increasing the computational complexity for parameter learning. Via simulations, the detection performances of classical, model-based, and model-free detectors are thoroughly compared to demonstrate the effectiveness of the supervised-learning approaches in this channel.

Includes members:
  Member "one bit ofdm receivers via deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "ofdm autoencoder for end to end learning of communications systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "golay layer limiting peak to average power ratio for ofdm based autoencoders")
  Member "multipair massive mimo relaying systems with one bit adc s and dac s":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mixed adc dac multipair massive mimo relaying systems performance analysis and power optimization")
  Member "one bit sphere decoding for uplink massive mimo systems with one bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral efficiency of one bit sigma delta massive mimo")
  Member "an introduction to deep learning for the physical layer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "channel estimation and performance analysis of one bit massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "performance analysis of quantized uplink massive mimo ofdm with oversampling under adjacent channel interference")
  Member "detection algorithms for communication systems using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "channel estimation in broadband millimeter wave mimo systems with few bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")

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)