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: "balancing the migration of virtual network functions with replications in data centers"
  Abstract: The Network Function Virtualization (NFV) paradigm is enabling flexibility, programmability and implementation of traditional network functions into generic hardware, in form of the so-called Virtual Network Functions (VNFs). Today, cloud service providers use Virtual Machines (VMs) for the instantiation of VNFs in the data center (DC) networks. To instantiate multiple VNFs in a typical scenario of Service Function Chains (SFCs), many important objectives need to be met simultaneously, such as server load balancing, energy efficiency and service execution time. The well-known \emph{VNF placement} problem requires solutions that often consider \emph{migration} of virtual machines (VMs) to meet this objectives. Ongoing efforts, for instance, are making a strong case for migrations to minimize energy consumption, while showing that attention needs to be paid to the Quality of Service (QoS) due to service interruptions caused by migrations. To balance the server allocation strategies and QoS, we propose using \emph{replications} of VNFs to reduce migrations in DC networks. We propose a Linear Programming (LP) model to study a trade-off between replications, which while beneficial to QoS require additional server resources, and migrations, which while beneficial to server load management can adversely impact the QoS. The results show that, for a given objective, the replications can reduce the number of migrations and can also enable a better server and data center network load balancing.

Connected through hyperedge HE-1 (induced by source paper "qoe management of multimedia streaming services in future networks a tutorial and survey"):
  with members:
    - "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"
    - "a novel qoe aware sdn enabled nfv based management architecture for future multimedia applications on 5g systems"
    - "mptcp meets fec supporting latency sensitive applications over heterogeneous networks"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "nfv and sdn key technology enablers for 5g networks"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "5g icn delivering icn services over 5g using network slicing"
    - "sdfog a software defined computing architecture for qos aware service orchestration over edge devices"

Connected through hyperedge HE-2 (induced by source paper "modeling of deep neural network dnn placement and inference in edge computing"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "a unified optimization approach for cnn model inference on integrated gpus"
    - "on device neural net inference with mobile gpus"
    - "dynamic space time scheduling for gpu inference"
    - "infaas a model less inference serving system"

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)