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: "learning to detect and mitigate cross layer attacks in wireless networks framework and applications"
  Abstract: Security threats such as jamming and route manipulation can have significant consequences on the performance of modern wireless networks. To increase the efficacy and stealthiness of such threats, a number of extremely challenging, cross-layer attacks have been recently unveiled. Although existing research has thoroughly addressed many single-layer attacks, the problem of detecting and mitigating cross-layer attacks still remains unsolved. For this reason, in this paper we propose a novel framework to analyze and address cross-layer attacks in wireless networks. Specifically, our framework consists of a detection and a mitigation component. The attack detection component is based on a Bayesian learning detection scheme that constructs a model of observed evidence to identify stealthy attack activities. The mitigation component comprises a scheme that achieves the desired trade-off between security and performance. We specialize and evaluate the proposed framework by considering a specific cross-layer attack that uses jamming as an auxiliary tool to achieve route manipulation. Simulations and experimental results obtained with a test-bed made up by USRP software-defined radios demonstrate the effectiveness of the proposed methodology.

Connected through hyperedge HE-1 (induced by source paper "machine learning for wireless communications in the internet of things a comprehensive survey"):
  with members:
    - "helper heterogeneous efficient low power radio for enabling ad hoc emergency public safety networks"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "over the air deep learning based radio signal classification"
    - "a non parametric multi stage learning framework for cognitive spectrum access in iot networks"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "fog computing survey of trends architectures requirements and research directions"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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 "big data goes small real time spectrum driven embedded wireless networking through deep learning in the rf loop"):
  with members:
    - "over the air deep learning based radio signal classification"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "an introduction to deep learning for the physical layer"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep architectures for modulation recognition"
    - "end to end learning from spectrum data a deep learning approach for wireless signal identification in spectrum monitoring applications"
    - "identity mappings in deep residual networks"
    - "spectrum sharing in mmwave cellular networks via cell association coordination and beamforming"

Connected through hyperedge HE-4 (induced by source paper "securing the internet of things in the age of machine learning and software defined networking"):
  with members:
    - "quality of information in mobile crowdsensing survey and research challenges"
    - "iot sentinel demo automated device type identification for security enforcement in iot"
    - "low power wide area networks an overview"
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "understanding modern banking ledgers through blockchain technologies future of transaction processing and smart contracts on the internet of money"
    - "device fingerprinting in wireless networks challenges and opportunities"
    - "incentive mechanisms for participatory sensing survey and research challenges"
    - "a survey on truth discovery"

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)