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 "on the effective capacity of an underwater acoustic channel under impersonation attack"):
  Abstract: This paper investigates the impact of authentication on effective capacity (EC) of an underwater acoustic (UWA) channel. Specifically, the UWA channel is under impersonation attack by a malicious node (Eve) present in the close vicinity of the legitimate node pair (Alice and Bob); Eve tries to inject its malicious data into the system by making Bob believe that she is indeed Alice. To thwart the impersonation attack by Eve, Bob utilizes the distance of the transmit node as the feature/fingerprint to carry out feature-based authentication at the physical layer. Due to authentication at Bob, due to lack of channel knowledge at the transmit node (Alice or Eve), and due to the threshold-based decoding error model, the relevant dynamics of the considered system could be modelled by a Markov chain (MC). Thus, we compute the state-transition probabilities of the MC, and the moment generating function for the service process corresponding to each state. This enables us to derive a closed-form expression of the EC in terms of authentication parameters. Furthermore, we compute the optimal transmission rate (at Alice) through gradient-descent (GD) technique and artificial neural network (ANN) method. Simulation results show that the EC decreases under severe authentication constraints (i.e., more false alarms and more transmissions by Eve). Simulation results also reveal that the (optimal transmission rate) performance of the ANN technique is quite close to that of the GD method.

Includes members:
  Member "impersonation detection in line of sight underwater acoustic sensor networks":
    also connected through other hyperedges:
      (none)
  Member "physical layer authentication in mission critical mtc networks a security and delay performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "twin timescale radio resource management for ultra reliable and low latency vehicular networks")
  Member "channel impulse response based distributed physical layer authentication":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "physical layer authentication in mission critical mtc networks a security and delay performance analysis")
  Member "energy efficiency optimization for mimo ofdm mobile multimedia communication systems with qos constraints":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neural networks in hybrid precoding for millimeter wave massive mimo systems")
  Member "effective capacity of delay constrained cognitive radio links exploiting primary feedback":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "effective capacity in wireless networks a comprehensive survey")
  Member "effective capacity of two hop wireless communication systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "simultaneous wireless information and power transfer for decode and forward multihop relay systems in energy constrained iot networks")
  Member "effective capacity analysis of cognitive radio channels for quality of service provisioning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the energy and data storage management in energy harvesting wireless communications")
  Member "using the physical layer for wireless authentication in time variant channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "performance of statistical and machine learning techniques for physical layer authentication")

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)