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 "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"):
  Abstract: Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with a huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play a pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML- and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML- and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "game theory for multi access edge computing survey use cases and future trends":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "a deep q learning method for downlink power allocation in multi cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "resource management in wireless networks via multi agent deep reinforcement learning")
  Member "machine learning in iot security current solutions and future challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "security privacy in iot using machine learning blockchain threats countermeasures")
  Member "toward intelligent vehicular networks a machine learning framework":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "limits lightweight machine learning for iot systems with resource limitations")
  Member "distributed learning for channel allocation over a shared spectrum":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "game of thrones fully distributed learning for multi player bandits")
  Member "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")
  Member "deep learning for radio resource allocation in multi cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")

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)