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 "impact of distributed processing on power consumption for iot based surveillance applications"):
  Abstract: With the rapid proliferation of connected devices in the Internet of Things (IoT), the centralized cloud solution faces several challenges, out of which, there is an overwhelming consensus to put energy efficiency at the top of the research agenda. In this paper, we evaluate the impact of demand splitting over heterogeneous processing resources in an IoT platform, supported by Fog and Cloud infrastructure. We develop a Mixed Integer Linear Programming (MILP) model to study the gains of splitting resource intensive demands among IoT nodes, Fog devices and Cloud servers. A surveillance application is considered, which consists of multiple smart cameras capable of capturing and analyzing real-time video streams. The PON access network aggregates IoT layer demands for processing in the Fog, or the Cloud which is accessed through the IP/WDM network. For typical video analysis workloads, the results show that splitting medium demand sizes among IoT and Fog resources yields a total power consumption saving of up to 32%, even if they can host only 10% of the total workload and this can reach 93% for lower number of demands, compared to the centralized cloud solution. However, the gains in power savings from splitting decreases as the number of splits increases. Keywords: IoT surveillance, PON, energy efficiency, fog, distributed processing.

Includes members:
  Member "big data analytics for wireless and wired network design a survey":
    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 "energy efficient service distribution in internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and 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 "the internet of things fog and cloud continuum integration and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fog to cloud and network coded based architecture minimizing data download time for smart mobility")
  Member "optimized energy aware 5g network function virtualization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)