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 "polar coding for the wiretap broadcast channel with multiple messages"):
  Abstract: A polar coding scheme is proposed for the Wiretap Broadcast Channel with two legitimate receivers and one eavesdropper. We consider a model in which the transmitter wishes to send different confidential (and non-confidential) information to each legitimate receiver. First, we compare two inner-bounds on the achievable region of this model from the literature, and then we design a polar coding scheme that achieves the stronger one. In the proposed scheme, the encoding uses polar-based Marton's coding, where one inner-layer must be reliably decoded by both legitimate receivers, and each receiver must decode its own corresponding outer-layer. Due to the non-degradedness condition of the broadcast channel, the encoder builds a chaining construction that induces bidirectional dependencies between adjacent blocks. Indeed, these dependencies can occur between different encoding layers of different blocks. The use of two secret-keys, whose length are negligible in terms of rate, are required to prove that the polar code satisfies the strong secrecy condition.

Includes members:
  Member "polar coding for common message only wiretap broadcast channel":
    also connected through other hyperedges:
      (none)
  Member "secrecy capacity region of some classes of wiretap broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secrecy rates for a channel with two senders and two receivers")
  Member "multi receiver wiretap channel with public and confidential messages":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secrecy rates for a channel with two senders and two receivers")
  Member "polar codes are optimal for lossy source coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "polarization for arbitrary discrete memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polar codes and their quantum domain counterparts")
  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-5 (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)