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: "frequency synchronization for uplink massive mimo systems"
  Abstract: In this paper, we propose a frequency synchronization scheme for multiuser orthogonal frequency division multiplexing (OFDM) uplink with a large-scale uniform linear array (ULA) at base station (BS) by exploiting the angle information of users. Considering that the incident signal at BS from each user can be restricted within a certain angular spread, the proposed scheme could perform carrier frequency offset (CFO) estimation for each user individually through a \textit{joint spatial-frequency alignment} procedure and can be completed efficiently with the aided of fast Fourier transform (FFT). A multi-branch receive beamforming is further designed to yield an equivalent single user transmission model for which the conventional single-user channel estimation and data detection can be carried out. To make the study complete, the theoretical performance analysis of the CFO estimation is also conducted. We further develop a user grouping scheme to deal with the unexpected scenarios that some users may not be separated well from the spatial domain. Finally, various numerical results are provided to verify the proposed studies.

Connected through hyperedge HE-1 (induced by source paper "a survey on deep learning based techniques for modeling and estimation of massivemimo channels"):
  with members:
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "deep learning for ul dl channel calibration in generic massive mimo systems"
    - "deep learning for tdd and fdd massive mimo mapping channels in space and frequency"
    - "sum spectral efficiency maximization in massive mimo systems benefits from deep learning"
    - "deep learning for csi feedback based on superimposed coding"
    - "compressed csi feedback with learned measurement matrix for mmwave massive mimo"
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"

Connected through hyperedge HE-2 (induced by source paper "vulnerabilities of massive mimo systems to pilot contamination attacks"):
  with members:
    - "optimal transmit antenna selection for massive mimo wiretap channels"
    - "massive mimo has unlimited capacity"
    - "exploiting full duplex receivers for achieving secret communications in multiuser miso networks"
    - "linear precoding of data and artificial noise in secure massive mimo systems"
    - "massive mimo ten myths and one critical question"
    - "individual secrecy for broadcast channels with receiver side information"
    - "5g cellular key enabling technologies and research challenges"
    - "physical layer security in massive mimo"

Connected through hyperedge HE-3 (induced by source paper "performance analysis of multi cell millimeter wave massive mimo networks with low precision adcs"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "a hardware efficient analog network structure for hybrid precoding in millimeter wave systems"
    - "a comparison of hybrid beamforming and digital beamforming with low resolution adcs for multiple users and imperfect csi"
    - "secure massive mimo systems with limited rf chains"
    - "on performance of quantized transceiver in multiuser massive mimo downlinks"
    - "spectral and energy efficiency of multi pair massive mimo relay network with hybrid processing"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "base station selection for massive mimo networks with two stage precoding"

Connected through hyperedge HE-4 (induced by source paper "high mobility wideband massive mimo communications doppler compensation analysis and scaling law"):
  with members:
    - "a new view of multi user hybrid massive mimo non orthogonal angle division multiple access"
    - "high mobility ofdm downlink transmission with large scale antenna array"
    - "angle domain doppler pre compensation for high mobility ofdm uplink with a massive ula"
    - "directivity beamwidth tradeoff of massive mimo uplink beamforming for high speed train communication"
    - "bdma for millimeter wave terahertz massive mimo transmission with per beam synchronization"

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)