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: "energy efficiency challenges of 5g small cell networks"
  Abstract: The deployment of a large number of small cells poses new challenges to energy efficiency, which has often been ignored in fifth generation (5G) cellular networks. While massive multiple-input multiple outputs (MIMO) will reduce the transmission power at the expense of higher computational cost, the question remains as to which computation or transmission power is more important in the energy efficiency of 5G small cell networks. Thus, the main objective in this paper is to investigate the computation power based on the Landauer principle. Simulation results reveal that more than 50% of the energy is consumed by the computation power at 5G small cell BS's. Moreover, the computation power of 5G small cell BS can approach 800 watt when the massive MIMO (e.g., 128 antennas) is deployed to transmit high volume traffic. This clearly indicates that computation power optimization can play a major role in the energy efficiency of small cell networks.

Connected through hyperedge HE-1 (induced by source paper "neural networks in hybrid precoding for millimeter wave massive mimo systems"):
  with members:
    - "deep learning based channel estimation for beamspace mmwave massive mimo systems"
    - "energy efficiency of mmwave massive mimo precoding with low resolution dacs"
    - "an introduction to deep learning for the physical layer"
    - "energy efficiency optimization of 5g radio frequency chain systems"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"
    - "deep residual learning for image recognition"
    - "5g ultra dense cellular networks"

Connected through hyperedge HE-2 (induced by source paper "performance analysis of urban mmwave multi hop v2v communications with shifted exponential distribution headway"):
  with members:
    - "5g software defined vehicular networks"
    - "multi hop links quality analysis of 5g enabled vehicular networks"
    - "energy efficiency optimization of 5g radio frequency chain systems"
    - "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"
    - "energy efficiency optimization for mimo ofdm mobile multimedia communication systems with qos constraints"

Connected through hyperedge HE-3 (induced by source paper "energy efficiency of generalized spatial modulation aided massive mimo systems"):
  with members:
    - "machine learning inspired energy efficient hybrid precoding for mmwave massive mimo systems"
    - "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-4 (induced by source paper "joint optimization of computation and communication power in multi user massive mimo systems"):
  with members:
    - "energy efficient transceiver design for hybrid sub array architecture mimo systems"
    - "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "energy efficiency optimization of 5g radio frequency chain systems"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "hybrid block diagonalization for massive multiuser mimo systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "impact of transceiver power consumption on the energy efficiency of zero forcing detector in massive mimo 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.NI(Networking and Internet Architecture)