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 "edge intelligence the confluence of edge computing and artificial intelligence"):
  Abstract: Along with the rapid developments in communication technologies and the surge in the use of mobile devices, a brand-new computation paradigm, Edge Computing, is surging in popularity. Meanwhile, Artificial Intelligence (AI) applications are thriving with the breakthroughs in deep learning and the many improvements in hardware architectures. Billions of data bytes, generated at the network edge, put massive demands on data processing and structural optimization. Thus, there exists a strong demand to integrate Edge Computing and AI, which gives birth to Edge Intelligence. In this paper, we divide Edge Intelligence into AI for edge (Intelligence-enabled Edge Computing) and AI on edge (Artificial Intelligence on Edge). The former focuses on providing more optimal solutions to key problems in Edge Computing with the help of popular and effective AI technologies while the latter studies how to carry out the entire process of building AI models, i.e., model training and inference, on the edge. This paper provides insights into this new inter-disciplinary field from a broader perspective. It discusses the core concepts and the research road-map, which should provide the necessary background for potential future research initiatives in Edge Intelligence.

Includes members:
  Member "convergence of edge computing and deep learning a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hivemind a scalable and serverless coordination control platform for uav swarms")
  Member "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pbgen partial binarization of deconvolution based generators for edge intelligence")
  Member "a graph neural network approach for scalable wireless power control":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "topology aware deep learning for wireless network optimization")
  Member "edge intelligence paving the last mile of artificial intelligence with edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse optimization for green edge ai inference")
  Member "scan a scalable neural networks framework towards compact and efficient models":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scalable deep neural networks via low rank matrix factorization")
  Member "vehicular edge computing via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges")
  Member "openei an open framework for edge intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on edge computing systems and tools")
  Member "uav communications for 5g and beyond recent advances and future trends":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "physical layer security for noma transmission in mmwave drone networks")

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)