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 "wireless edge computing with latency and reliability guarantees"):
  Abstract: Edge computing is an emerging concept based on distributing computing, storage, and control services closer to end network nodes. Edge computing lies at the heart of the fifth generation (5G) wireless systems and beyond. While current state-of-the-art networks communicate, compute, and process data in a centralized manner (at the cloud), for latency and compute-centric applications, both radio access and computational resources must be brought closer to the edge, harnessing the availability of computing and storage-enabled small cell base stations in proximity to the end devices. Furthermore, the network infrastructure must enable a distributed edge decision-making service that learns to adapt to the network dynamics with minimal latency and optimize network deployment and operation accordingly. This article will provide a fresh look to the concept of edge computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling ultra-reliable and low-latency edge computing services for mission-critical applications such as virtual reality (VR), vehicle-to-everything (V2X), edge artificial intelligence (AI), and so forth. Furthermore, several case studies where the edge is key are explored followed by insights and prospect for future work.

Includes members:
  Member "distributed federated learning for ultra reliable low latency vehicular communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed dual coordinate ascent in general tree networks and its application in federated learning")
  Member "machine learning at the wireless edge distributed stochastic gradient descent over the air":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wireless federated learning with local differential privacy")
  Member "how reliable and capable is multi connectivity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "wireless mesh networking with devices equipped with multi connectivity")
  Member "a tutorial on beam management for 3gpp nr at mmwave frequencies":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "coverage analysis of uavs in millimeter wave networks a stochastic geometry approach")
  Member "uav communications for 5g and beyond recent advances and future trends":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "physical layer security for noma transmission in mmwave drone networks")
  Member "coded federated computing in wireless networks with straggling devices and imperfect csi":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "machine learning in the air")
  Member "ultra reliable communication in 5g mmwave networks a risk sensitive approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave networks")
  Member "on device federated learning via blockchain and its latency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "edge intelligence paving the last mile of artificial intelligence with edge computing")

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)