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: "hybrid precoding based millimeter wave massive mimo noma with simultaneous wireless information and power transfer"
  Abstract: Non-orthogonal multiple access (NOMA) has been recently considered in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. In addition, simultaneous wireless information and power transfer (SWIPT) is a promising solution to maximize the energy efficiency. In this paper, for the first time, we investigate the integration of SWIPT in mmWave massive MIMO-NOMA systems. As mmWave massive MIMO will likely use hybrid precoding (HP) to significantly reduce the number of required radio-frequency (RF) chains without an obvious performance loss, where the fully digital precoder is decomposed into a high-dimensional analog precoder and a low-dimensional digital precoder, we propose to apply SWIPT in HP-based MIMO-NOMA systems, where each user can extract both information and energy from the received RF signals by using a power splitting receiver. Specifically, the cluster-head selection (CHS) algorithm is proposed to select one user for each beam at first, and then the analog precoding is designed according to the selected cluster heads for all beams. After that, user grouping is performed based on the correlation of users' equivalent channels. Then, the digital precoding is designed by selecting users with the strongest equivalent channel gain in each beam. Finally, the achievable sum rate is maximized by jointly optimizing power allocation for mmWave massive MIMO-NOMA and power splitting factors for SWIPT, and an iterative optimization algorithm is developed to solve the non-convex problem. Simulation results show that the proposed HP-based MIMO-NOMA with SWIPT can achieve higher spectrum and energy efficiency compared with HP-based MIMO-OMA with SWIPT.

Connected through hyperedge HE-1 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt"):
  with members:
    - "joint optimization of computation and communication power in multi user massive mimo systems"
    - "energy efficient transceiver design for hybrid sub array architecture mimo systems"
    - "energy efficient power allocation in millimeter wave massive mimo with non orthogonal multiple access"
    - "joint base station clustering and beamforming for non orthogonal multicast and unicast transmission with backhaul constraints"
    - "joint beamforming and broadcasting in massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "on the spectral efficiency and security enhancements of noma assisted multicast unicast streaming"
    - "energy efficiency optimization of 5g radio frequency chain systems"

Connected through hyperedge HE-2 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul"):
  with members:
    - "optimized base station cache allocation for cloud radio access network with multicast backhaul"
    - "safeguarding millimeter wave communications against randomly located eavesdroppers"
    - "energy efficient power allocation in millimeter wave massive mimo with non orthogonal multiple access"
    - "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference"
    - "robust an aided beamforming and power splitting design for secure miso cognitive radio with swipt"
    - "energy efficient joint congestion control and resource optimization in heterogeneous cloud radio access networks"
    - "content centric sparse multicast beamforming for cache enabled cloud ran"
    - "secure communications in millimeter wave ad hoc networks"

Connected through hyperedge HE-3 (induced by source paper "a survey of rate optimal power domain noma schemes for enabling technologies of future wireless networks"):
  with members:
    - "generalized coordinated multipoint gcomp enabled noma outage capacity and power allocation"
    - "what role can noma play in massive mimo"
    - "uav data collection over noma backscatter networks uav altitude and trajectory optimization"
    - "uav communications for 5g and beyond recent advances and future trends"
    - "when full duplex wireless meets non orthogonal multiple access opportunities and challenges"
    - "on optimizing vlc networks for downlink multi user transmission a survey"
    - "beam design and user scheduling for nonorthogonal multiple access with multiple antennas based on pareto optimality"
    - "spatial modulation assisted multi antenna non orthogonal multiple access"

Connected through hyperedge HE-4 (induced by source paper "deep learning based channel estimation for massive mimo with mixed resolution adcs"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "deep learning in physical layer communications"
    - "mixed adc dac multipair massive mimo relaying systems performance analysis and power optimization"
    - "performance analysis of mixed adc massive mimo systems over rician fading channels"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "mixed adc massive mimo"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "uplink achievable rate for massive mimo systems with low resolution adc"

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)