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 "energy efficient and resilient infrastructure for fog computing health monitoring applications"):
  Abstract: In this paper, we propose a resilient energy efficient and fog computing infrastructure for health monitoring applications. We design the infrastructure to be resilient against server failures under two scenarios; without geographical constraints and with geographical constraints. We consider a heart monitoring application where patients send their 30-seconds recording of Electrocardiogram (ECG) signal for processing, analysis and decision making at both primary and backup servers. A Mixed Integer Linear Programming (MILP) model is used to optimize the number and locations of the primary and backup processing servers so that the energy consumption of both the processing and networking equipment are minimized. The results show that considering geographical constraints yields a network energy consumption increase by up to 9% compared to without geographical constraint. The results also show that, increasing the number of processing servers that can be served at each candidate node can reduce the energy consumption of networking equipment besides reducing the rate of energy increase of networking equipment due to increasing level of demand.

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 "patient centric cellular networks optimization using big data analytics":
    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 "bounds for energy efficient survivable ip over wdm networks with network coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient virtual machines placement over cloud fog network architecture")
  Member "energy efficiency of fog computing health monitoring applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g networks")
  Member "server placement with shared backups for disaster resilient clouds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "securing virtual network function placement with high availability guarantees")

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)