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 "the secrecy rate region of the broadcast channel"):
  Abstract: In this paper, we consider a scenario where a source node wishes to broadcast two confidential messages for two respective 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 equivocation rate at the eavesdropper. We first study the general (non-degraded) broadcast channel with confidential messages. We present an inner bound on the secrecy capacity region for this model. The inner bound coding scheme is based on a combination of random binning and the Gelfand-Pinsker bining. This scheme matches the Marton's inner bound on the broadcast channel without confidentiality constraint. We further study the situation where the channels are degraded. For the degraded broadcast channel with confidential messages, we present the secrecy capacity region. Our achievable coding scheme is based on Cover's superposition scheme and random binning. We refer to this scheme as Secret Superposition Scheme. In this scheme, we show that randomization in the first layer increases the secrecy rate of the second layer. This capacity region matches the capacity region of the degraded broadcast channel without security constraint. It also matches the secrecy capacity for the conventional wire-tap channel. Our converse proof is based on a combination of the converse proof of the conventional degraded broadcast channel and Csiszar lemma. Finally, we assume that the channels are Additive White Gaussian Noise (AWGN) and show that secret superposition scheme with Gaussian codebook is optimal. The converse proof is based on the generalized entropy power inequality.

Includes members:
  Member "interference assisted secret communication":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "jammer assisted resource allocation in secure ofdma with untrusted users")
  Member "capacity bounds for broadcast channels with confidential messages":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal secure gdof of symmetric gaussian wiretap channel with a helper")
  Member "on the secure degrees of freedom in the k user gaussian interference channel":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "friendly jamming in a mimo wiretap interference network a nonconvex game approach")
  Member "success exponent of wiretapper a tradeoff between secrecy and reliability":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "exact correct decoding exponent of the wiretap channel decoder")
  Member "secure communication over fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "parallel bcc with one common and two confidential messages and imperfect csit")
  Member "secrecy capacity of the wiretap channel with noisy feedback":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the commitment capacity of unfair noisy channels")
  Member "an extremal inequality motivated by multiterminal information theoretic problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "vector gaussian successive refinement with degraded side information")
  Member "on the secrecy capacity of fading channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "parallel bcc with one common and two confidential messages and imperfect csit")

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)