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: "securing the internet of things in the age of machine learning and software defined networking"
  Abstract: The Internet of Things (IoT) realizes a vision where billions of interconnected devices are deployed just about everywhere, from inside our bodies to the most remote areas of the globe. As the IoT will soon pervade every aspect of our lives and will be accessible from anywhere, addressing critical IoT security threats is now more important than ever. Traditional approaches where security is applied as an afterthought and as a “patch” against known attacks are insufficient. Indeed, next-generation IoT challenges will require a new secure-by-design vision, where threats are addressed proactively and IoT devices learn to dynamically adapt to different threats. To this end, machine learning (ML) and software-defined networking (SDN) will be key to provide both reconfigurability and intelligence to the IoT devices. In this paper, we first provide a taxonomy and survey the state of the art in IoT security research, and offer a roadmap of concrete research challenges related to the application of ML and SDN to address existing and next-generation IoT security threats.

Connected through hyperedge HE-1 (induced by source paper "security privacy in iot using machine learning blockchain threats countermeasures"):
  with members:
    - "machine learning in iot security current solutions and future challenges"
    - "on accurate and reliable anomaly detection for gas turbine combustors a deep learning approach"
    - "security services using blockchains a state of the art survey"
    - "a survey on the security of blockchain systems"
    - "secure mobile crowdsensing with deep learning"
    - "a first look at identity management schemes on the blockchain"
    - "blockchain as a service an autonomous privacy preserving decentralized architecture for deep learning"
    - "sok security and privacy in machine learning"

Connected through hyperedge HE-2 (induced by source paper "active link obfuscation to thwart link flooding attacks for internet of things"):
  with members:
    - "on the interplay of link flooding attacks and traffic engineering"
    - "a novel framework for modeling and mitigating distributed link flooding attacks"

Connected through hyperedge HE-3 (induced by source paper "machine learning in iot security current solutions and future challenges"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "machine learning ddos detection for consumer internet of things devices"
    - "ai based two stage intrusion detection for software defined iot networks"
    - "machine intelligence techniques for next generation context aware wireless networks"
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep reinforcement learning a brief survey"

Connected through hyperedge HE-4 (induced by source paper "blockchain for the internet of things present and future"):
  with members:
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "block4forensic an integrated lightweight blockchain framework for forensics applications of connected vehicles"
    - "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-5 (induced by source paper "toward operator to waveform 5g radio access network slicing"):
  with members:
    - "big data goes small real time spectrum driven embedded wireless networking through deep learning in the rf loop"
    - "low complexity distributed radio access network slicing algorithms and experimental results"
    - "wnos an optimization based wireless network operating system"
    - "network slicing for 5g with sdn nfv concepts architectures and challenges"
    - "network slicing to enable scalability and flexibility in 5g mobile networks"
    - "a survey on low latency towards 5g ran core network and caching solutions"
    - "survey on network virtualization hypervisors for software defined networking"
    - "from network sharing to multi tenancy the 5g network slice broker"

Connected through hyperedge HE-6 (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"
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "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-7 (induced by source paper "jam sessions analysis and experimental evaluation of advanced jamming attacks in mimo networks"):
  with members:
    - "machine learning for wireless communications in the internet of things a comprehensive survey"
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "subverting massive mimo by smart jamming"

Connected through hyperedge HE-8 (induced by source paper "incentme effective mechanism design to stimulate crowdsensing participants with uncertain mobility"):
  with members:
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "taskme multi task allocation in mobile crowd sensing"
    - "incentive mechanisms for participatory sensing survey and research challenges"
    - "mining users significant driving routes with low power sensors"
    - "on the approximability of budget feasible mechanisms"
    - "budget feasible mechanisms"

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.CR(Cryptography and Security)