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: "modeling and dimensioning of a virtualized mme for 5g mobile networks"
  Abstract: Network function virtualization is considered one of the key technologies for developing future mobile networks. In this paper, we propose a theoretical framework to evaluate the performance of a Long-Term Evolution (LTE) virtualized mobility management entity (vMME) hosted in a data center. This theoretical framework consists of 1) a queuing network to model the vMME in a data center and 2) analytic expressions to estimate the overall mean system delay and the signaling workload to be processed by the vMME. We validate our mathematical model by simulation. One direct use of the proposed model is vMME dimensioning, i.e., to compute the number of vMME processing instances to provide a target system delay given the number of users in the system. Additionally, the paper includes a scalability analysis of the system. In our study, we consider the billing model and a data center setup of Amazon Elastic Compute Cloud service and estimate the processing time of MME processing instances for different LTE control procedures experimentally. For the considered setup, our results show that the vMME is scalable for signaling workloads up to 37 000 LTE control procedures per second for a target mean system delay of 1 ms. The system design and database performance assumed imposes this limit in the system scalability.

Connected through hyperedge HE-1 (induced by source paper "vnf placement and resource allocation for the support of vertical services in 5g networks"):
  with members:
    - "arbitration among vertical services"
    - "optimal virtual network function placement in multi cloud service function chaining architecture"
    - "network slicing based 5g and future mobile networks mobility resource management and challenges"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"

Connected through hyperedge HE-2 (induced by source paper "joint communication and control for wireless autonomous vehicular platoon systems"):
  with members:
    - "deep learning for reliable mobile edge analytics in intelligent transportation systems"
    - "millimeter wave v2v communications distributed association and beam alignment"
    - "coverage analysis of a vehicular network modeled as cox process driven by poisson line process"
    - "surveillance and intervention of infrastructure free mobile communications a new wireless security paradigm"
    - "coverage and rate analysis for millimeter wave cellular networks"

Connected through hyperedge HE-3 (induced by source paper "sdn architectural enabler for reliable communication over millimeter wave 5g networks"):
  with members:
    - "enabling millimeter wave 5g networks for massive iot applications"
    - "reliable video streaming over mmwave with multi connectivity and network coding"
    - "millimeter wave cellular networks a mac layer perspective"
    - "millimeter wave channel modeling and cellular capacity evaluation"

Connected through hyperedge HE-4 (induced by source paper "analytical modeling for virtualized network functions"):
  with members:
    - "nfv state of the art challenges and implementation in next generation mobile networks vepc"

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)