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: "on service chaining strategies using virtual network functions in operator networks"
  Abstract: Abstract   Network functions (e.g., firewalls, load balancers, etc.) have been traditionally provided through proprietary hardware appliances. Often, hardware appliances need to be hardwired back-to-back to form a service chain providing chained network functions. These hardware appliances cannot be provisioned on-demand since they are statically embedded in the network topology, making creation, insertion, modification, upgrade, and removal of service chains complex, and also slowing down service innovation. Hence, network operators are starting to deploy Virtual Network Functions (VNFs), which are virtualized over commodity hardware. VNFs can be deployed in Data Centers (DCs) or in Network Function Virtualization (NFV)-capable network elements (nodes) such as routers and switches. NFV-capable nodes and DCs together form a Network-enabled Cloud (NeC) that helps to facilitate the dynamic service chaining required to support today’s evolving network traffic and its service demands. In this study, we focus on the VNF service-chain placement and traffic routing problem, and build a model for placing a VNF service chain while minimizing network-resource consumption. Further, we study the network-resource consumption of various service-chaining strategies, which can be a NeC having a varying number of NFV-capable nodes or a proprietary hardware solution. Our results indicate that a NeC having a DC and NFV-capable nodes can significantly reduce network-resource consumption.

Connected through hyperedge HE-1 (induced by source paper "an energy driven network function virtualization for multi domain software defined networks"):
  with members:
    - "multi timescale online optimization of network function virtualization for service chaining"
    - "network function virtualization state of the art and research challenges"

Connected through hyperedge HE-2 (induced by source paper "auto scaling network resources using machine learning to improve qos and reduce cost"):
  with members:
    - "design and analysis of deadline and budget constrained autoscaling dbca algorithm for 5g mobile networks"
    - "power consumption of virtualization technologies an empirical investigation"

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)