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 "when machine learning meets big data a wireless communication perspective"):
  Abstract: We have witnessed an exponential growth in commercial data services, which has lead to the 'big data era'. Machine learning, as one of the most promising artificial intelligence tools of analyzing the deluge of data, has been invoked in many research areas both in academia and industry. The aim of this article is twin-fold. Firstly, we briefly review big data analysis and machine learning, along with their potential applications in next-generation wireless networks. The second goal is to invoke big data analysis to predict the requirements of mobile users and to exploit it for improving the performance of "social network-aware wireless". More particularly, a unified big data aided machine learning framework is proposed, which consists of feature extraction, data modeling and prediction/online refinement. The main benefits of the proposed framework are that by relying on big data which reflects both the spectral and other challenging requirements of the users, we can refine the motivation, problem formulations and methodology of powerful machine learning algorithms in the context of wireless networks. In order to characterize the efficiency of the proposed framework, a pair of intelligent practical applications are provided as case studies: 1) To predict the positioning of drone-mounted areal base stations (BSs) according to the specific tele-traffic requirements by gleaning valuable data from social networks. 2) To predict the content caching requirements of BSs according to the users' preferences by mining data from social networks. Finally, open research opportunities are identified for motivating future investigations.

Includes members:
  Member "deep learning in physical layer communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep reinforcement learning for online offloading in wireless powered mobile edge computing networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence")
  Member "big data analytics machine learning and artificial intelligence in next generation wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "blockchain for 5g and beyond networks a state of the art survey")
  Member "wireless communications in the era of big data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "artificial intelligence enabled wireless networking for 5g and beyond recent advances and future challenges")

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)