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 "computation offloading and content caching delivery in vehicular edge computing a survey"):
  Abstract: Autonomous Vehicles (AVs) generated a plethora of data prior to support various vehicle applications. Thus, a big storage and high computation platform is necessary, and this is possible with the presence of Cloud Computing (CC). However, the computation for vehicular networks at the cloud computing suffers from several drawbacks, such as latency and cost, due to the proximity issue. As a solution, the computing capability has been recently proposed at the edge of vehicular networks, which is known as Vehicle Edge Computing (VEC). This leads to other open problems for vehicles to offload and compute data at edge nodes, and also how data is cached in edge nodes and then disseminated to other vehicles. In this paper, we initially present an overview of VEC architectures including types of layers, fog nodes, communication technologies and also vehicle applications, which are used in data offloading and dissemination scenarios. Since the mobility is critical on the VEC performance, the mobility model used in the VEC scenario is also discussed. We extensively review the Computation Offloading (ComOf) techniques as well as Content Caching and Delivery (CachDel) approaches for VEC. We finally highlight some key research challenges, issues and future works in the paper

Includes members:
  Member "spectrum resource management for multi access edge computing in autonomous vehicular networks":
    also connected through other hyperedges:
      (none)
  Member "adaptive learning based task offloading for vehicular edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "distributed task replication for vehicular edge computing performance analysis and learning based algorithm")
  Member "concurrent transmission scheduling for perceptual data sharing in mmwave vehicular networks":
    also connected through other hyperedges:
      (none)
  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 "vehicular edge computing via deep reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reinforcement learning for autonomous internet of things model applications and challenges")
  Member "mobile edge intelligence and computing for the internet of vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse optimization for green edge ai inference")
  Member "sdn based resource management for autonomous vehicular networks a multi access edge computing approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secel privacy preserving verifiable and fault tolerant edge learning for autonomous vehicles")
  Member "joint offloading and resource allocation in vehicular edge computing and networks":
    also connected through other hyperedges:
      (none)

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)