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 routing and network coding in core networks"):
  Abstract: We propose network coding as an energy efficient data transmission technique in core networks with non-bypass and bypass routing approaches. The improvement in energy efficiency is achieved through reduction in the traffic flows passing through intermediate nodes. A mixed integer linear program (MILP) is developed to optimize the use of network resources, and the results show that our proposed network coding approach introduces up to 33% power savings for the non-bypass case compared with the conventional architectures. For the bypass case, 28% power savings are obtained considering futuristic network components power consumption. A heuristic based on the minimum hop count routing shows power savings comparable to the MILP results. Furthermore, we study how the change in network topology affects the savings produced by network coding. The results show that the savings are proportional to the average hop count of the network topology. We also derive power consumption analytic bounds and closed form expressions for networks that implement network coding and thus also verify the results obtained by the MILP model.

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 efficient iot virtualization framework with peer to peer networking and processing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "patient centric hetnets powered by machine learning and big data analytics for 6g 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 "optimization based linear network coding for general connections of continuous flows":
    also connected through other hyperedges:
      (none)
  Member "overlay protection against link failures using network coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the multiple unicast capacity of 3 source 3 terminal directed acyclic networks")
  Member "network protection codes against link failures using network coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bounds for energy efficient survivable ip over wdm networks with network coding")

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)