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: "5g ultra dense cellular networks"
  Abstract: Traditional ultra-dense wireless networks are recommended as a complement for cellular networks and are deployed in partial areas, such as hotspot and indoor scenarios. Based on the massive multiple-input multi-output (MIMO) antennas and the millimeter wavecommunication technologies, the 5G ultra-dense cellular network is proposed to deploy in overall cellular scenarios. Moreover, a distribution network architecture is presented for 5G ultra-dense cellular networks. Furthermore, the backhaul network capacity and the backhaul energy efficiency of ultra-dense cellular networks are investigated to answer an important question, i.e., how much densification can be deployed for 5G ultra-dense cellular networks. Simulation results reveal that there exist densification limits for 5G ultra-dense cellualr networks with backhaul network capacity and backhaul energy efficiency constraints.

Connected through hyperedge HE-1 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "deep learning based channel estimation for massive mimo with mixed resolution adcs"
    - "deep learning based wireless resource allocation with application to vehicular networks"
    - "using grouped linear prediction and accelerated reinforcement learning for online content caching"
    - "optimal and scalable caching for 5g using reinforcement learning of space time popularities"
    - "multi agent learning for cooperative large scale caching networks"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "random caching based cooperative transmission in heterogeneous wireless networks"

Connected through hyperedge HE-2 (induced by source paper "distributed resource allocation for network slicing of bandwidth and computational resource"):
  with members:
    - "distributed resource allocation for network slicing over licensed and unlicensed bands"
    - "joint optimization of computation and communication power in multi user massive mimo systems"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "computing resource allocation in three tier iot fog networks a joint optimization approach combining stackelberg game and matching"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "spatial spectrum and energy efficiency of random cellular networks"

Connected through hyperedge HE-3 (induced by source paper "the potential short and long term disruptions and transformative impacts of 5g and beyond wireless networks lessons learnt from the development of a 5g testbed environment"):
  with members:
    - "the current state of affairs in 5g security and the main remaining security challenges"
    - "blockchain for internet of things a survey"
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "a speculative study on 6g"
    - "5g nr jamming spoofing and sniffing threat assessment and mitigation"
    - "a concise review of 5g new radio capabilities for directional access at mmwave frequencies"
    - "mutual heterogeneous signcryption schemes for 5g network slicings"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"

Connected through hyperedge HE-4 (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 challenges of 5g small cell 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"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "a novel jt comp scheme in 5g fractal small cell networks"):
  with members:
    - "ultra reliable uav communication using altitude and cooperation diversity"
    - "downlink small cell base station cooperation strategy in fractal small cell networks"
    - "wireless fractal cellular networks"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "user mobility evaluation for 5g small cell networks based on individual mobility model"
    - "a stochastic geometry framework for analyzing pairwise cooperative 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-6 (induced by source paper "energy efficient optimization of wireless powered 5g full duplex cellular networks a mean field game approach"):
  with members:
    - "tradeoff between delay and physical layer security in wireless networks"
    - "distributed user association in energy harvesting small cell networks a probabilistic bandit model"
    - "ultra dense small cell networks turning density into energy efficiency"
    - "toward a tractable delay analysis in ultra dense networks"
    - "fronthauling for 5g lte u ultra dense cloud small cell networks"
    - "heterogeneous cellular networks with spatio temporal traffic delay analysis and scheduling"
    - "energy harvesting small cell networks feasibility deployment and operation"
    - "mean field energy games in wireless networks"

Connected through hyperedge HE-7 (induced by source paper "dynamic model for network selection in next generation hetnets with memory affecting rational users"):
  with members:
    - "a learning based coexistence mechanism for laa lte based hetnets"
    - "energy minimization for wireless communication with rotary wing uav"
    - "blocking probability and spatial throughput characterization for cellular enabled uav network with directional antenna"
    - "downlink and uplink cell association with traditional macrocells and millimeter wave small cells"
    - "green 5g heterogeneous networks through dynamic small cell operation"
    - "physical layer security in millimeter wave cellular networks"
    - "tractable model for rate in self backhauled millimeter wave cellular networks"
    - "what will 5g be"

Connected through hyperedge HE-8 (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"
    - "energy efficiency challenges of 5g small cell 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 wireless backhaul networks challenges and research advances"
    - "energy efficiency optimization for mimo ofdm mobile multimedia communication systems with qos constraints"

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)