human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "how much training is needed in one bit massive mimo systems at low snr"
  Abstract: This paper considers training-based transmissions in massive multi-input multi-output (MIMO) systems with one-bit analog-to-digital converters (ADCs). We assume that each coherent transmission block consists of a pilot training stage and a data transmission stage. The base station (BS) first employs the linear minimum mean-square-error (LMMSE) method to estimate the channel and then uses the maximum-ratio combining (MRC) receiver to detect the data symbols. We first obtain an approximate closed-form expression for the uplink achievable rate in the low SNR region. Then based on the result, we investigate the optimal training length that maximizes the sum spectral efficiency for two cases: i) The training power and the data transmission power are both optimized; ii) The training power and the data transmission power are equal. Numerical results show that, in contrast to conventional massive MIMO systems, the optimal training length in one-bit massive MIMO systems is greater than the number of users and depends on various parameters such as the coherence interval and the average transmit power. Also, unlike conventional systems, it is observed that in terms of sum spectral efficiency, there is relatively little benefit to separately optimizing the training and data power.

Connected through hyperedge HE-1 (induced by source paper "training for channel estimation in nonlinear multi antenna transceivers"):
  with members:
    - "beamforming with multiple one bit wireless transceivers"
    - "capacity of multiple one bit transceivers in a rayleigh environment"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "massive mu mimo ofdm downlink with one bit dacs and linear precoding"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "downlink achievable rate analysis in massive mimo systems with one bit dacs"
    - "mmse precoder for massive mimo using 1 bit quantization"

Connected through hyperedge HE-2 (induced by source paper "full duplex massive mimo relaying systems with low resolution adcs"):
  with members:
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "spatio temporal waveform design for multiuser massive mimo downlink with 1 bit receivers"
    - "joint source relay design for full duplex mimo af relay systems"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "mixed adc massive mimo"
    - "mixed adc massive mimo uplink in frequency selective channels"
    - "minimum ber precoding in 1 bit massive mimo systems"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"

Connected through hyperedge HE-3 (induced by source paper "low snr asymptotic rates of vector channels with one bit outputs"):
  with members:
    - "gaussian multiple access channels with one bit quantizer at the receiver"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "mixed adc massive mimo"
    - "channel estimation and uplink achievable rates in one bit massive mimo systems"
    - "one bit massive mimo channel estimation and high order modulations"
    - "joint channel and data estimation for large mimo systems with low precision adcs"

Connected through hyperedge HE-4 (induced by source paper "achievable uplink rates for massive mimo with coarse quantization"):
  with members:
    - "uplink performance of wideband massive mimo with one bit adcs"
    - "massive mimo with low resolution adcs"
    - "mixed adc massive mimo"
    - "on the spectral efficiency of massive mimo systems with low resolution adcs"
    - "channel estimation and uplink achievable rates in one bit massive mimo systems"
    - "analysis of massive mimo with hardware impairments and different channel models"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "capacity analysis of one bit quantized mimo systems with transmitter channel state information"

Connected through hyperedge HE-5 (induced by source paper "downlink achievable rate analysis in massive mimo systems with one bit dacs"):
  with members:
    - "nonlinear 1 bit precoding for massive mu mimo with higher order modulation"
    - "minimum ber precoding in 1 bit massive mimo systems"
    - "mmse precoder for massive mimo using 1 bit quantization"
    - "uplink achievable rate for massive mimo systems with low resolution adc"
    - "massive mimo with non ideal arbitrary arrays hardware scaling laws and circuit aware design"
    - "multipair full duplex relaying with massive arrays and linear processing"
    - "energy and spectral efficiency of very large multiuser mimo systems"
    - "pilot contamination and precoding in multi cell tdd systems"

Question: Please tell me which class the center node 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)