human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "performance of statistical and machine learning techniques for physical layer authentication"):
  Abstract: In this paper we consider authentication at the physical layer, in which the authenticator aims at distinguishing a legitimate supplicant from an attacker on the basis of the characteristics of the communication channel. Authentication is performed over a set of parallel wireless channels affected by time-varying fading at the presence of a malicious attacker, whose channel has a spatial correlation with the supplicant's one. We first propose the use of two different statistical decision methods, and we prove that using a large number of references (in the form of channel estimates) affected by different levels of time-varying fading is not beneficial from a security point of view. We then propose to exploit classification methods based on machine learning. In order to face the worst case of an authenticator provided with no forged messages during training, we consider one-class classifiers. When instead the training set includes some forged messages, we resort to more conventional binary classifiers, considering the cases in which such messages are either labelled or not. For the latter case, we exploit clustering algorithms to label the training set. The performance of both nearest neighbor (NN) and support vector machine (SVM) classification techniques is assessed. Through numerical examples, we show that under the same probability of false alarm, one-class classification (OCC) algorithms achieve the lowest probability of missed detection when a small spatial correlation exists between the main channel and the adversary one, while statistical methods are advantageous when the spatial correlation between the two channels is large.

Includes members:
  Member "machine learning for intelligent authentication in 5g and beyond wireless networks":
    also connected through other hyperedges:
      (none)
  Member "statistical and machine learning based decision techniques for physical layer authentication":
    also connected through other hyperedges:
      (none)
  Member "learning aided physical layer authentication as an intelligent process":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "machine learning for intelligent authentication in 5g and beyond wireless networks")
  Member "application of machine learning for channel based message authentication in mission critical machine type communication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "statistical and machine learning based decision techniques for physical layer authentication")
  Member "analysis of channel based user authentication by key less and key based approaches":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "machine learning for intelligent authentication in 5g and beyond wireless networks")
  Member "physical layer security in heterogeneous cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "enhancing physical layer security of random caching in large scale multi antenna heterogeneous wireless networks")
  Member "using the physical layer for wireless authentication in time variant channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the effective capacity of an underwater acoustic channel under impersonation attack")

Question: Please tell me which class the source paper 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)