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: "application of machine learning in wireless networks key techniques and open issues"
  Abstract: As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.

Connected through hyperedge HE-1 (induced by source paper "fog computing systems state of the art research issues and future trends"):
  with members:
    - "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges"
    - "a roadmap toward the resilient internet of things for cyber physical systems"
    - "game theory for multi access edge computing survey use cases and future trends"
    - "a blockchain enabled trustless crowd intelligence ecosystem on mobile edge computing"
    - "machine learning for resource management in cellular and iot networks potentials current solutions and open challenges"
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "mitigating jamming attacks using energy harvesting"
    - "game theoretical modelling of network cybersecurity"

Connected through hyperedge HE-2 (induced by source paper "deep reinforcement learning based mode selection and resource allocation for cellular v2x communications"):
  with members:
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"
    - "federated learning strategies for improving communication efficiency"
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "deep reinforcement learning based mode selection and resource management for green fog radio access networks"
    - "deep reinforcement learning for resource allocation in v2v communications"
    - "network slicing in fog radio access networks issues and challenges"
    - "communication efficient learning of deep networks from decentralized data"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-3 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach"):
  with members:
    - "optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning"
    - "deep reinforcement learning based resource allocation for v2v communications"
    - "fog computing based radio access networks issues and challenges"
    - "joint user association and resource allocation in virtualized wireless networks"

Connected through hyperedge HE-4 (induced by source paper "risk sensitive task fetching and offloading for vehicular edge computing"):
  with members:
    - "ultra reliable and low latency vehicular transmission an extreme value theory approach"
    - "ultra reliable and low latency wireless communication tail risk and scale"
    - "a survey on mobile edge computing the communication perspective"
    - "backhaul aware interference management in the uplink of wireless small cell networks"

Connected through hyperedge HE-5 (induced by source paper "edge intelligence the confluence of edge computing and artificial intelligence"):
  with members:
    - "convergence of edge computing and deep learning a comprehensive survey"
    - "efficient hybrid network architectures for extremely quantized neural networks enabling intelligence at the edge"
    - "a graph neural network approach for scalable wireless power control"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "scan a scalable neural networks framework towards compact and efficient models"
    - "vehicular edge computing via deep reinforcement learning"
    - "openei an open framework for edge intelligence"
    - "uav communications for 5g and beyond recent advances and future trends"

Connected through hyperedge HE-6 (induced by source paper "a joint learning and communications framework for federated learning over wireless networks"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "energy efficient radio resource allocation for federated edge learning"
    - "fast uplink grant for noma a federated learning based approach"
    - "towards federated learning at scale system design"
    - "mobile edge computing blockchain and reputation based crowdsourcing iot federated learning a secure decentralized and privacy preserving system"
    - "when machine learning meets big data a wireless communication perspective"
    - "multi hop federated private data augmentation with sample compression"
    - "in edge ai intelligentizing mobile edge computing caching and communication by federated learning"

Connected through hyperedge HE-7 (induced by source paper "non uniform time step deep q network for carrier sense multiple access in heterogeneous wireless networks"):
  with members:
    - "distributive dynamic spectrum access through deep reinforcement learning a reservoir computing based approach"
    - "carrier sense multiple access for heterogeneous wireless networks using deep reinforcement learning"
    - "actor critic deep reinforcement learning for dynamic multichannel access"
    - "applications of deep reinforcement learning in communications and networking a survey"
    - "deep reinforcement learning for dynamic multichannel access in wireless networks"
    - "deep reinforcement learning an overview"
    - "proactive resource management for lte in unlicensed spectrum a deep learning perspective"
    - "deep multi user reinforcement learning for distributed dynamic spectrum access"

Connected through hyperedge HE-8 (induced by source paper "reinforcement learning based scheduling algorithm for optimizing age of information in ultra reliable low latency networks"):
  with members:
    - "ultra reliable low latency vehicular networks taming the age of information tail"
    - "age of information and throughput in a shared access network with heterogeneous traffic"

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)