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 "finite snr regime analysis of the gaussian wiretap multiple access channel"):
  Abstract: In this work, we consider a K-user Gaussian wiretap multiple-access channel (GW-MAC) in which each transmitter has an independent confidential message for the receiver. There is also an external eavesdropper who intercepts the communications. The goal is to transmit the messages reliably while keeping them confidential from the eavesdropper. To accomplish this goal, two different approaches have been proposed in prior works, namely, i.i.d. Gaussian random coding and real alignment. However, the former approach fails at moderate and high SNR regimes as its achievable result does not grow with SNR. On the other hand, while the latter approach gives a promising result at the infinite SNR regime, its extension to the finite-SNR regime is a challenging task. To fill the gap between the performance of the existing approaches, in this work, we establish a new scheme in which, at the receiver's side, it utilizes an extension of the compute-and-forward decoding strategy and at the transmitters' side it exploits lattice alignment, cooperative jamming, and i.i.d. random codes. For the proposed scheme, we derive a new achievable bound on sum secure rate which scales with log(SNR) and hence it outperforms the i.i.d. Gaussian codes in moderate and high SNR regimes. We evaluate the performance of our scheme, both theoretically and numerically. Furthermore, we show that our sum secure rate achieves the optimal sum secure degrees of freedom in the infinite-SNR regime.

Includes members:
  Member "compute and forward can buy secrecy cheap":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "preserving confidentiality in the gaussian broadcast channel using compute and forward")
  Member "secure degrees of freedom of one hop wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards an algebraic network information theory simultaneous joint typicality decoding")
  Member "the approximate sum capacity of the symmetric gaussian k user interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "private information retrieval over gaussian mac")
  Member "on the secure degrees of freedom of the multiple access channel":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adding common randomness can remove the secrecy constraints in communication networks")
  Member "compute and forward harnessing interference through structured codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "private information retrieval over gaussian mac")
  Member "on the role of mmse estimation in approaching the information theoretic limits of linear gaussian channels shannon meets wiener":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lattice coding for downlink multiuser transmission")

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)