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 "ergodic secret alignment"):
  Abstract: In this paper, we introduce two new achievable schemes for the fading multiple access wiretap channel (MAC-WT). In the model that we consider, we assume that perfect knowledge of the state of all channels is available at all the nodes in a causal fashion. Our schemes use this knowledge together with the time varying nature of the channel model to align the interference from different users at the eavesdropper perfectly in a one-dimensional space while creating a higher dimensionality space for the interfering signals at the legitimate receiver hence allowing for better chance of recovery. While we achieve this alignment through signal scaling at the transmitters in our first scheme (scaling based alignment (SBA)), we let nature provide this alignment through the ergodicity of the channel coefficients in the second scheme (ergodic secret alignment (ESA)). For each scheme, we obtain the resulting achievable secrecy rate region. We show that the secrecy rates achieved by both schemes scale with SNR as 1/2log(SNR). Hence, we show the sub-optimality of the i.i.d. Gaussian signaling based schemes with and without cooperative jamming by showing that the secrecy rates achieved using i.i.d. Gaussian signaling with cooperative jamming do not scale with SNR. In addition, we introduce an improved version of our ESA scheme where we incorporate cooperative jamming to achieve higher secrecy rates. Moreover, we derive the necessary optimality conditions for the power control policy that maximizes the secrecy sum rate achievable by our ESA scheme when used solely and with cooperative jamming.

Includes members:
  Member "interference alignment for secrecy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adding common randomness can remove the secrecy constraints in communication networks")
  Member "on the secure degrees of freedom of the multiple access channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adding common randomness can remove the secrecy constraints in communication networks")
  Member "real interference alignment with real numbers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "linear degrees of freedom of mimo broadcast channels with reconfigurable antennas in the absence of csit")
  Member "secure degrees of freedom for gaussian channels with interference structured codes outperform gaussian signaling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "preserving confidentiality in the gaussian broadcast channel using compute and forward")
  Member "ergodic interference alignment":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "the interference channel revisited aligning interference by adjusting receive antenna separation")
  Member "k user interference channels achievable secrecy rate and degrees of freedom":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the secure degrees of freedom of the k user interference channel with delayed csit")
  Member "on the secure degrees of freedom of wireless x networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "on the secure degrees of freedom of the k user interference channel with delayed csit")
  Member "the gaussian multiple access wire tap channel":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an achievable region for the multiple access wiretap channels with confidential and open messages")

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)