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: "scalable and accurate modeling of the millimeter wave channel"
  Abstract: Communication at millimeter wave (mmWave) frequencies is one of the main novelties introduced in the 5th generation (5G) of cellular networks. The opportunities and challenges associated with such high frequencies have stimulated a number of studies that rely on simulation for the evaluation of the proposed solutions. The accuracy of simulations largely depends on that of the channel model, but popular channel models for mmWaves, such as the Spatial Channel Models (SCMs), have high computational complexity and limit the scalability of the scenarios. This paper profiles the implementation of a widely-used SCM model for mmWave frequencies, and proposes a simplified version of the 3GPP SCM that reduces the computation time by up to 12.5 times while providing essentially the same distributions of several metrics, such as the Signal-to-Interference-plus-Noise Ratio (SINR) in large scale scenarios. We also give insights on the use cases in which using a simplified model can still yield valid results.

Connected through hyperedge HE-1 (induced by source paper "implementation of a spatial channel model for ns 3"):
  with members:
    - "nr u and wigig coexistence in 60 ghz bands"
    - "new radio physical layer abstraction for system level simulations of 5g networks"
    - "end to end simulation of 5g mmwave networks"
    - "ieee 802 11ay based mmwave wlans design challenges and solutions"
    - "multi sector and multi panel performance in 5g mmwave cellular networks"
    - "impact of channel models on the end to end performance of mmwave cellular networks"
    - "modeling and analyzing millimeter wave cellular systems"
    - "ns 3 implementation of the 3gpp mimo channel model for frequency spectrum above 6 ghz"

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)