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: "physical layer authentication in mission critical mtc networks a security and delay performance analysis"
  Abstract: We study the detection and delay performance impacts of a feature-based physical layer authentication (PLA) protocol in mission-critical machine-type communication (MTC) networks. The PLA protocol uses generalized likelihood-ratio testing based on the line-of-sight (LOS), single-input multiple-output channel-state information in order to mitigate impersonation attempts from an adversary node. We study the detection performance, develop a queueing model that captures the delay impacts of erroneous decisions in the PLA (i.e., the false alarms and missed detections), and model three different adversary strategies: data injection, disassociation, and Sybil attacks. Our main contribution is the derivation of analytical delay performance bounds that allow us to quantify the delay introduced by PLA that potentially can degrade the performance in mission-critical MTC networks. For the delay analysis, we utilize tools from stochastic network calculus. Our results show that with a sufficient number of receive antennas (approx. 4-8) and sufficiently strong LOS components from legitimate devices, PLA is a viable option for securing mission-critical MTC systems, despite the low latency requirements associated to corresponding use cases. Furthermore, we find that PLA can be very effective in detecting the considered attacks, and in particular, it can significantly reduce the delay impacts of disassociation and Sybil attacks.

Connected through hyperedge HE-1 (induced by source paper "on the effective capacity of an underwater acoustic channel under impersonation attack"):
  with members:
    - "impersonation detection in line of sight underwater acoustic sensor networks"
    - "channel impulse response based distributed physical layer authentication"
    - "energy efficiency optimization for mimo ofdm mobile multimedia communication systems with qos constraints"
    - "effective capacity of delay constrained cognitive radio links exploiting primary feedback"
    - "effective capacity of two hop wireless communication systems"
    - "effective capacity analysis of cognitive radio channels for quality of service provisioning"
    - "using the physical layer for wireless authentication in time variant channels"

Connected through hyperedge HE-2 (induced by source paper "twin timescale radio resource management for ultra reliable and low latency vehicular networks"):
  with members:
    - "on low resolution adcs in practical 5g millimeter wave massive mimo systems"
    - "fractional programming for communication systems part i power control and beamforming"
    - "a two stage allocation scheme for delay sensitive services in dense vehicular networks"
    - "cross layer optimization for ultra reliable and low latency radio access networks"
    - "low latency millimeter wave communications traffic dispersion or network densification"
    - "on the feasibility of wireless energy transfer using massive antenna arrays"
    - "blocklength limited performance of relaying under quasi static rayleigh channels"
    - "reliable and efficient autonomous driving the need for heterogeneous vehicular networks"

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)