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: "machine learning inspired energy efficient hybrid precoding for mmwave massive mimo systems"
  Abstract: Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.

Connected through hyperedge HE-1 (induced by source paper "energy efficiency of generalized spatial modulation aided massive mimo systems"):
  with members:
    - "energy efficiency challenges of 5g small cell networks"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"
    - "5g ultra dense cellular networks"
    - "5g wireless backhaul networks challenges and research advances"
    - "impact of transceiver power consumption on the energy efficiency of zero forcing detector in massive mimo systems"
    - "what will 5g be"
    - "optimal design of energy efficient multi user mimo systems is massive mimo the answer"

Connected through hyperedge HE-2 (induced by source paper "hybrid precoding based millimeter wave massive mimo noma with simultaneous wireless information and power transfer"):
  with members:
    - "secure swipt networks based on a non linear energy harvesting model"
    - "energy efficient power allocation in millimeter wave massive mimo with non orthogonal multiple access"
    - "joint beamforming and power splitting control in downlink cooperative swipt noma systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "a general mimo framework for noma downlink and uplink transmission based on signal alignment"
    - "simultaneous wireless information and power transfer in modern communication systems"
    - "joint transmit beamforming and receive power splitting for miso swipt systems"

Connected through hyperedge HE-3 (induced by source paper "noma meets finite resolution analog beamforming in massive mimo and millimeter wave networks"):
  with members:
    - "power domain non orthogonal multiple access noma in 5g systems potentials and challenges"
    - "design of massive mimo noma with limited feedback"
    - "resource management in non orthogonal multiple access networks for 5g and beyond"
    - "massive mimo combining with switches"
    - "on the performance of non orthogonal multiple access in 5g systems with randomly deployed users"

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)