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 role of artificial noise in training and data transmission for secret communications"):
  Abstract: This work considers the joint design of training and data transmission in physical-layer secret communication systems, and examines the role of artificial noise (AN) in both of these phases. In particular, AN in the training phase is used to prevent the eavesdropper from obtaining accurate channel state information (CSI) whereas AN in the data transmission phase can be used to mask the transmission of the confidential message. By considering AN-assisted training and secrecy beamforming schemes, we first derive bounds on the achievable secrecy rate and obtain a closed-form approximation that is asymptotically tight at high SNR. Then, by maximizing the approximate achievable secrecy rate, the optimal power allocation between signal and AN in both training and data transmission phases is obtained for both conventional and AN-assisted training based schemes. We show that the use of AN is necessary to achieve a high secrecy rate at high SNR, and its use in the training phase can be more efficient than that in the data transmission phase when the coherence time is large. However, at low SNR, the use of AN provides no advantage since CSI is difficult to obtain in this case. Numerical results are presented to verify our theoretical claims.

Includes members:
  Member "power allocation and time domain artificial noise design for wiretap ofdm with discrete inputs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an overview of physical layer security with finite alphabet signaling")
  Member "two way training for discriminatory channel estimation in wireless mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a semiblind two way training method for discriminatory channel estimation in mimo systems")
  Member "on secrecy rate of the generalized artificial noise assisted secure beamforming for wiretap channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "physical layer security in the finite blocklength regime over fading channels")
  Member "secure transmission with artificial noise over fading channels achievable rate and optimal power allocation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure massive mimo communication with low resolution dacs")
  Member "secure transmission with multiple antennas ii the mimome wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi antenna jamming in covert communication")
  Member "towards the secrecy capacity of the gaussian mimo wire tap channel the 2 2 1 channel":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "interplay between noma and other emerging technologies a survey")
  Member "the secrecy capacity of the mimo wiretap channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "angle aware user cooperation for secure massive mimo in rician fading channel")
  Member "multiuser mimo achievable rates with downlink training and channel state feedback":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beyond dirty paper coding for multi antenna broadcast channel with partial csit a rate splitting approach")

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.IT(Information Theory)