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: "secrecy rate region of the broadcast channel with an eavesdropper"
  Abstract: In this paper, we consider a scenario where a source node wishes to broadcast two confidential messages to two receivers, while a wire-tapper also receives the transmitted signal. This model is motivated by wireless communications, where individual secure messages are broadcast over open media and can be received by any illegitimate receiver. The secrecy level is measured by the equivocation rate at the eavesdropper. We first study the general (non-degraded) broadcast channel with an eavesdropper. We present an inner bound on the secrecy capacity region for this model. This inner bound is based on a combination of random binning, and the Gelfand-Pinsker binning. We further study the situation in which the channels are degraded. For the degraded broadcast channel with an eavesdropper, we present the secrecy capacity region. Our achievable coding scheme is based on Covers superposition scheme and random binning. We refer to this scheme as the Secret Superposition Scheme. Our converse proof is based on a combination of the converse proof of the conventional degraded broadcast channel and Csiszar Lemma. We then assume that the channels are Additive White Gaussian Noise (AWGN) and show that the Secret Superposition Scheme with Gaussian codebook is optimal. The converse proof is based on Costas entropy power inequality. Finally, we use a broadcast strategy for the slowly fading wire-tap channel when only the eavesdroppers channel is fixed and known at the transmitter. We derive the optimum power allocation for the coding layers, which maximizes the total average rate.

Connected through hyperedge HE-1 (induced by source paper "individual secrecy for broadcast channels with receiver side information"):
  with members:
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "strong secrecy for cooperative broadcast channels"
    - "individual secrecy for the broadcast channel"
    - "secrecy capacity region of some classes of wiretap broadcast channels"
    - "capacity regions for broadcast channels with degraded message sets and message cognition under different secrecy constraints"
    - "on block security of regenerating codes at the mbr point for distributed storage systems"
    - "effective secrecy reliability confusion and stealth"
    - "new results on multiple input multiple output broadcast channels with confidential messages"

Connected through hyperedge HE-2 (induced by source paper "individual secrecy for the broadcast channel"):
  with members:
    - "broadcast channels with privacy leakage constraints"
    - "secure multiplex coding with dependent and non uniform multiple messages"
    - "strong secrecy for cooperative broadcast channels"
    - "individual secrecy for broadcast channels with receiver side information"
    - "secrecy capacity region of some classes of wiretap broadcast channels"
    - "capacity regions for broadcast channels with degraded message sets and message cognition under different secrecy constraints"
    - "effective secrecy reliability confusion and stealth"
    - "reliability and secrecy functions of the wiretap channel under cost constraint"

Connected through hyperedge HE-3 (induced by source paper "degraded compound multi receiver wiretap channels"):
  with members:
    - "3 receiver broadcast channels with common and confidential messages"
    - "the secrecy capacity region of the gaussian mimo multi receiver wiretap channel"
    - "a vector generalization of costa s entropy power inequality with applications"
    - "secrecy capacity of a class of broadcast channels with an eavesdropper"
    - "the secrecy rate region of the broadcast channel"

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.IT(Information Theory)