human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "price and performance of cloud hosted virtual network functions analysis and future challenges"):
  Abstract: The concept of Network Function Virtualization (NFV) has been introduced as a new paradigm in the recent few years. NFV offers a number of benefits including significantly increased maintainability and reduced deployment overhead. Several works have been done to optimize deployment (also called embedding) of virtual network functions (VNFs). However, no work to date has looked into optimizing the selection of cloud instances for a given VNF and its specific requirements. In this paper, we evaluate the performance of VNFs when embedded on different Amazon EC2 cloud instances. Specifically, we evaluate three VNFs (firewall, IDS, and NAT) in terms of arrival packet rate, resources utilization, and packet loss. Our results indicate that performance varies across instance types, departing from the intuition of "you get what you pay for" with cloud instances. We also find out that CPU is the critical resource for the tested VNFs, although their peak packet processing capacities differ considerably from each other. Finally, based on the obtained results, we identify key research challenges related to VNF instance selection and service chain provisioning.

Includes members:
  Member "transferable knowledge for low cost decision making in cloud environments":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection")
  Member "serverless computing current trends and open problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "serverless straggler mitigation using local error correcting codes")
  Member "daleel simplifying cloud instance selection using machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection")

Question: Please tell me which class the source paper 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)