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: "fingerprints in the ether using the physical layer for wireless authentication"
  Abstract: The wireless medium contains domain-specific information that can be used to complement and enhance traditional security mechanisms. In this paper we propose ways to exploit the fact that, in a typically rich scattering environment, the radio channel response decorrelates quite rapidly in space. Specifically, we describe a physical-layer algorithm that combines channel probing (M complex frequency response samples over a bandwidth W) with hypothesis testing to determine whether current and prior communication attempts are made by the same user (same channel response). In this way, legitimate users can be reliably authenticated and false users can be reliably detected. To evaluate the feasibility of our algorithm, we simulate spatially variable channel responses in real environments using the WiSE ray-tracing tool; and we analyze the ability of a receiver to discriminate between transmitters (users) based on their channel frequency responses in a given office environment. For several rooms in the extremities of the building we considered, we have confirmed the efficacy of our approach under static channel conditions. For example, measuring five frequency response samples over a bandwidth of 100 MHz and using a transmit power of 100 mW, valid users can be verified with 99% confidence while rejecting false users with greater than 95% confidence.

Connected through hyperedge HE-1 (induced by source paper "learning aided physical layer attacks against multicarrier communications in iot"):
  with members:
    - "communication without interception defense against deep learning based modulation detection"
    - "an overview of physical layer security with finite alphabet signaling"
    - "an introduction to deep learning for the physical layer"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "infovae information maximizing variational autoencoders"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "a survey on wireless security technical challenges recent advances and future trends"

Connected through hyperedge HE-2 (induced by source paper "physical layer authentication in mission critical mtc networks a security and delay performance analysis"):
  with members:
    - "application of machine learning for channel based message authentication in mission critical machine type communication"
    - "channel impulse response based distributed physical layer authentication"
    - "physical layer authentication for mission critical machine type communication using gaussian mixture model based clustering"
    - "secure transmission of delay sensitive data over wireless fading channels"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-3 (induced by source paper "application of machine learning for channel based message authentication in mission critical machine type communication"):
  with members:
    - "providing physical layer security for mission critical machine type communication"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-4 (induced by source paper "physical layer authentication for mission critical machine type communication using gaussian mixture model based clustering"):
  with members:
    - "providing physical layer security for mission critical machine type communication"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-5 (induced by source paper "providing physical layer security for mission critical machine type communication"):
  with members:
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-6 (induced by source paper "device fingerprinting in wireless networks challenges and opportunities"):
  with members:
    - "an empirical study of passive 802 11 device fingerprinting"
    - "using the physical layer for wireless authentication in time variant channels"
    - "mining behavioral groups in large wireless lans"

Connected through hyperedge HE-7 (induced by source paper "exploiting channel diversity in secret key generation from multipath fading randomness"):
  with members:
    - "the sender excited secret key agreement model capacity reliability and secrecy exponents"
    - "key generation using external source excitation capacity reliability and secrecy exponent"
    - "information theoretically secret key generation for fading wireless channels"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-8 (induced by source paper "secret key agreement with channel state information at the transmitter"):
  with members:
    - "secrecy via sources and channels"
    - "wiretap channel with causal state information"
    - "lecture notes on network information theory"
    - "3 receiver broadcast channels with common and confidential messages"

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)