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 "beamforming with multiple one bit wireless transceivers"):
  Abstract: Classical beamforming techniques rely on highly linear transmitters and receivers to allow phase-coherent combining at the transmitter and receiver. The transmitter uses beamforming to steer signal power towards the receiver, and the receiver uses beamforming to gather and coherently combine the signals from multiple receiver antennas. When the transmitters and receivers are instead constrained for power and cost reasons to be non-linear one-bit devices, the potential advantages and performance metrics associated with beamforming are not as well understood. We define beamforming at the transmitter as a codebook design problem to maximize the minimum distance between codewords. We define beamforming at the receiver as the maximum likelihood detector of the transmitted codeword. We show that beamforming with one-bit transceivers is a constellation design problem, and that we can come within a few dB SNR of the capacity attained by linear transceivers.

Includes members:
  Member "uplink performance of wideband massive mimo with one bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "training for channel estimation in nonlinear multi antenna transceivers")
  Member "downlink achievable rate analysis in massive mimo systems with one bit dacs":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral efficiency of one bit sigma delta massive mimo")
  Member "channel estimation and uplink achievable rates in one bit massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "one bit ofdm receivers via deep learning")
  Member "capacity analysis of one bit quantized mimo systems with transmitter channel state information":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "near maximum likelihood detector and channel estimator for uplink multiuser massive mimo systems with one bit adcs":
    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 "quantized massive mu mimo ofdm uplink":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "analysis of 1 bit output noncoherent fading channels in the low snr regime":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "new equivalent model of quantizer with noisy input and its application for adc resolution determination in an uplink mimo receiver")

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)