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 aided physical layer authentication as an intelligent process"
  Abstract: Performance of the existing physical layer authentication schemes could be severely affected by the imperfect estimates and variations of the communication link attributes used. The commonly adopted static hypothesis testing for physical layer authentication faces significant challenges in time-varying communication channels due to the changing propagation and interference conditions, which are typically unknown at the design stage. To circumvent this impediment, we propose an adaptive physical layer authentication scheme based on machine-learning as an intelligent process to learn and utilize the complex and time-varying environment, and hence to improve the reliability and robustness of physical layer authentication. Explicitly, a physical layer attribute fusion model based on a kernel machine is designed for dealing with multiple attributes without requiring the knowledge of their statistical properties. By modeling the physical layer authentication as a linear system, the proposed technique directly reduces the authentication scope from a combined N-dimensional feature space to a single dimensional (scalar) space, hence leading to reduced authentication complexity. By formulating the learning (training) objective of the physical layer authentication as a convex problem, an adaptive algorithm based on kernel least-mean-square is then proposed as an intelligent process to learn and track the variations of multiple attributes, and therefore to enhance the authentication performance. Both the convergence and the authentication performance of the proposed intelligent authentication process are theoretically analyzed. Our simulations demonstrate that our solution significantly improves the authentication performance in time-varying environments.

Connected through hyperedge HE-1 (induced by source paper "performance of statistical and machine learning techniques for physical layer authentication"):
  with members:
    - "machine learning for intelligent authentication in 5g and beyond wireless networks"
    - "statistical and machine learning based decision techniques for physical layer authentication"
    - "application of machine learning for channel based message authentication in mission critical machine type communication"
    - "analysis of channel based user authentication by key less and key based approaches"
    - "physical layer security in heterogeneous cellular networks"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-2 (induced by source paper "machine learning for intelligent authentication in 5g and beyond wireless networks"):
  with members:
    - "machine learning for wireless connectivity and security of cellular connected uavs"
    - "analysis of channel based user authentication by key less and key based approaches"
    - "machine learning methods for attack detection in the smart grid"

Connected through hyperedge HE-3 (induced by source paper "statistical and machine learning based decision techniques for physical layer authentication"):
  with members:
    - "application of machine learning for channel based message authentication in mission critical machine type communication"
    - "analysis of channel based user authentication by key less and key based approaches"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-4 (induced by source paper "fast authentication and progressive authorization in large scale iot how to leverage ai for security enhancement"):
  with members:
    - "deep learning for signal authentication and security in massive internet of things systems"

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)