human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sdn based resource management for autonomous vehicular networks a multi access edge computing approach"
  Abstract: Enabling high-definition (HD)-map-assisted cooperative driving among autonomous vehicles (AVs) to improve the navigation safety faces technical challenges due to increased communication traffic volume for data dissemination and increased number of computing/storing tasks on AVs. In this article, a new architecture that combines multi-access edge computing (MEC) and software-defined networking (SDN) is proposed for flexible resource management and enhanced resource utilization. With MEC, the interworking of multiple wireless access technologies can be realized to exploit the diversity gain over a wide range of radio spectrum, and at the same time, computing/storing tasks of an AV are collaboratively processed by servers and other AVs. Moreover, by enabling SDN and network function virtualization (NFV) control modules at each cloud-computing and MEC server, an efficient resource allocation framework is proposed to enhance global resource sharing among different network infrastructures. A case study is presented to demonstrate the effectiveness of the proposed resource allocation framework.

Connected through hyperedge HE-1 (induced by source paper "secel privacy preserving verifiable and fault tolerant edge learning for autonomous vehicles"):
  with members:
    - "advances and open problems in federated learning"
    - "efficient decentralized deep learning by dynamic model averaging"
    - "federated learning for ultra reliable low latency v2v communications"
    - "towards an intelligent edge wireless communication meets machine learning"
    - "how to backdoor federated learning"
    - "a survey on mobile edge computing the communication perspective"
    - "mobile edge computing a survey on architecture and computation offloading"
    - "deep models under the gan information leakage from collaborative deep learning"

Connected through hyperedge HE-2 (induced by source paper "computation offloading and content caching delivery in vehicular edge computing a survey"):
  with members:
    - "spectrum resource management for multi access edge computing in autonomous vehicular networks"
    - "adaptive learning based task offloading for vehicular edge computing systems"
    - "concurrent transmission scheduling for perceptual data sharing in mmwave vehicular networks"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "vehicular edge computing via deep reinforcement learning"
    - "mobile edge intelligence and computing for the internet of vehicles"
    - "joint offloading and resource allocation in vehicular edge computing and networks"
    - "millimeter wave v2v communications distributed association and beam alignment"

Connected through hyperedge HE-3 (induced by source paper "spectrum resource management for multi access edge computing in autonomous vehicular networks"):
  with members:
    - "business case and technology analysis for 5g low latency applications"
    - "vehicular communications a network layer perspective"
    - "optimal distributed control for platooning via sparse coprime factorizations"
    - "user association for load balancing in heterogeneous cellular networks"

Question: Please tell me which class the center node 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)