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 "toward intelligent network optimization in wireless networking an auto learning framework"):
  Abstract: In wireless communication systems (WCSs), the network optimization problems (NOPs) play an important role in maximizing system performances by setting appropriate network configurations. When dealing with NOPs by using conventional optimization methodologies, there exist the following three problems: human intervention, model invalid, and high computation complexity. As such, in this article we propose an auto-learning framework (ALF) to achieve intelligent and automatic network optimization by using machine learning (ML) techniques. We review the basic concepts of ML techniques, and propose their rudimentary employment models in WCSs, including automatic model construction, experience replay, efficient trial-and-error, RL-driven gaming, complexity reduction, and solution recommendation. We hope these proposals can provide new insights and motivations in future researches for dealing with NOPs in WCSs by using ML techniques.

Includes members:
  Member "cognitive lpwan towards intelligent wireless services in hybrid low power wide area networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cognitive information measurements a new perspective")
  Member "machine learning for networking workflow advances and opportunities":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning for intelligent optical networks a comprehensive survey")
  Member "massive mimo and small cells improving energy efficiency by optimal soft cell coordination":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a data aided channel estimation scheme for decoupled systems in heterogeneous networks")
  Member "coalitional game theory for communication networks a tutorial":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coalition formation game for delay reduction in instantly decodable network coding assisted d2d communications")

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)