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 "resilient service embedding in iot networks"):
  Abstract: The Internet of Things (IoT) has been applied to a large number of heterogeneous devices and is used in the deployment of a variety of applications on the basis of its distributed open architecture. The majority of these IoT devices are battery-powered and are interconnected via a wireless network. IoT devices may be used to carry out critical tasks. Thus, the IoT network requires a resilient architecture that supports semantic search, failure discovery, data recovery, and dynamic and autonomous network maintenance. In this paper, we present a new resilience scheme for IoT networks. We evaluate the proposed scheme in terms of its power consumption and data delivery time, and then compare the results with those of recent resilience schemes such as schemes based on redundancy and replication. The proposed framework was optimized using mixed integer linear programming and real-time heuristics were developed, thus embedding a virtual layer into a physical layer based on a service-oriented architecture (SOA). The proposed framework offers different combinations of packet resilience in terms of recovering the lost data by using end- to-end mechanisms. We further analyzed these schemes by investigating the power consumption, data delivery time, and network overhead of these techniques. The results showed that the proposed splitting technique enhanced the network performance by reducing the power consumption and the data delivery time of service embedding by selecting energy-efficient nodes and routes in IoT networks.

Includes members:
  Member "service embedding in iot networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks")
  Member "big data analytics for wireless and wired network design a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks")
  Member "patient centric cellular networks optimization using big data analytics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks")
  Member "bounds for energy efficient survivable ip over wdm networks with network coding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient virtual machines placement over cloud fog network architecture")
  Member "energy efficient iot virtualization framework with peer to peer networking and processing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks")
  Member "energy efficiency of server centric pon data center architecture for fog computing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks")
  Member "optimized energy aware 5g network function virtualization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks")

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)