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: "machine learning for wireless communications in the internet of things a comprehensive survey"
  Abstract: The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing and dynamic spectrum access will soon become essential components of the IoT wireless communication process. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection etc) to reach the network's optimal operating point. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms. This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions on machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Finally, we provide a series of research challenges associated with the applications of machine learning techniques for IoT wireless communications.

Connected through hyperedge HE-1 (induced by source paper "machine learning in the air"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "machine learning at the wireless edge distributed stochastic gradient descent over the air"
    - "deep learning for distributed optimization applications to wireless resource management"
    - "federated learning over wireless fading channels"
    - "deepmimo a generic deep learning dataset for millimeter wave and massive mimo applications"
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "toward intelligent vehicular networks a machine learning framework"
    - "coded federated computing in wireless networks with straggling devices and imperfect csi"

Connected through hyperedge HE-2 (induced by source paper "blockchain for the internet of things present and future"):
  with members:
    - "block4forensic an integrated lightweight blockchain framework for forensics applications of connected vehicles"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "smart contract based access control for the internet of things"
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "lsb a lightweight scalable blockchain for iot security and privacy"
    - "learning to detect and mitigate cross layer attacks in wireless networks framework and applications"
    - "blockchain a distributed solution to automotive security and privacy"
    - "providing privacy safety and security in iot based transactive energy systems using distributed ledgers"

Connected through hyperedge HE-3 (induced by source paper "deepradioid real time channel resilient optimization of deep learning based radio fingerprinting algorithms"):
  with members:
    - "big data goes small real time spectrum driven embedded wireless networking through deep learning in the rf loop"
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "deep learning based dynamic watermarking for secure signal authentication in the internet of things"
    - "device fingerprinting in wireless networks challenges and opportunities"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-4 (induced by source paper "jam sessions analysis and experimental evaluation of advanced jamming attacks in mimo networks"):
  with members:
    - "securing the internet of things in the age of machine learning and software defined networking"
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "subverting massive mimo by smart jamming"

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)