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: "millimeter wave evolution for 5g cellular networks"
  Abstract: Triggered by the explosion of mobile traffic, 5G (5th Generation) cellular network requires evolution to increase the system rate 1000 times higher than the current systems in 10 years. Motivated by this common problem, there are several studies to integrate mm-wave access into current cellular networks as multi-band heterogeneous networks to exploit the ultra-wideband aspect of the mm-wave band. The authors of this paper have proposed comprehensive architecture of cellular networks with mm-wave access, where mm-wave small cell basestations and a conventional macro basestation are connected to Centralized-RAN (C-RAN) to effectively operate the system by enabling power efficient seamless handover as well as centralized resource control including dynamic cell structuring to match the limited coverage of mm-wave access with high traffic user locations via user-plane/control-plane splitting. In this paper, to prove the effectiveness of the proposed 5G cellular networks with mm-wave access, system level simulation is conducted by introducing an expected future traffic model, a measurement based mm-wave propagation model, and a centralized cell association algorithm by exploiting the C-RAN architecture. The numerical results show the effectiveness of the proposed network to realize 1000 times higher system rate than the current network in 10 years which is not achieved by the small cells using commonly considered 3.5 GHz band. Furthermore, the paper also gives latest status of mm-wave devices and regulations to show the feasibility of using mm-wave in the 5G systems.

Connected through hyperedge HE-1 (induced by source paper "proactive received power prediction using machine learning and depth images for mmwave networks"):
  with members:
    - "small scale local area and transitional millimeter wave propagation for 5g communications"
    - "rapid fading due to human blockage in pedestrian crowds at 5g millimeter wave frequencies"
    - "analysis of human body blockage in urban millimeter wave cellular communications"
    - "millimeter wave human blockage at 73 ghz with a simple double knife edge diffraction model and extension for directional antennas"
    - "stochastic geometry modeling and analysis of multi tier millimeter wave cellular networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "long term recurrent convolutional networks for visual recognition and description"

Connected through hyperedge HE-2 (induced by source paper "a multi traffic inter cell interference coordination scheme in dense cellular networks"):
  with members:
    - "algorithms for enhanced inter cell interference coordination eicic in lte hetnets"
    - "on number of almost blank subframes in heterogeneous cellular networks"
    - "joint resource partitioning and offloading in heterogeneous cellular networks"
    - "enhanced intercell interference coordination challenges in heterogeneous networks"
    - "refim a practical interference management in heterogeneous wireless access networks"

Connected through hyperedge HE-3 (induced by source paper "where when and how mmwave is used in 5g and beyon"):
  with members:
    - "millimeter wave beamforming for wireless backhaul and access in small cell networks"

Connected through hyperedge HE-4 (induced by source paper "context information for fast cell discovery in mm wave 5g networks"):
  with members:
    - "adaptive beamwidth selection for contention based access periods in millimeter wave wlans"
    - "directional cell discovery in millimeter wave cellular networks"
    - "millimeter wave cellular wireless networks potentials and challenges"

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)