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 "achieving the secrecy capacity of wiretap channels using polar codes"):
  Abstract: Suppose that Alice wishes to send messages to Bob through a communication channel C1, but her transmissions also reach an eavesdropper Eve through another channel C2. This is the wiretap channel model introduced by Wyner in 1975. The goal is to design a coding scheme that makes it possible for Alice to communicate both reliably and securely. Reliability is measured in terms of Bob's probability of error in recovering the message, while security is measured in terms of the mutual information between the message and Eve's observations. Wyner showed that the situation is characterized by a single constant Cs, called the secrecy capacity, which has the following meaning: for all e >; 0, there exist coding schemes of rate R ≥ Cs-e that asymptotically achieve the reliability and security objectives. However, his proof of this result is based upon a random-coding argument. To date, despite consider able research effort, the only case where we know how to construct coding schemes that achieve secrecy capacity is when Eve's channel C2 is an erasure channel, or a combinatorial variation thereof. Polar codes were recently invented by Arikan; they approach the capacity of symmetric binary-input discrete memoryless channels with low encoding and decoding complexity. In this paper, we use polar codes to construct a coding scheme that achieves the secrecy capacity for a wide range of wiretap channels. Our construction works for any instantiation of the wiretap channel model, as long as both C1 and C2 are symmetric and binary-input, and C2 is degraded with respect to C1. Moreover, we show how to modify our construction in order to provide strong security, in the sense defined by Maurer, while still operating at a rate that approaches the secrecy capacity. In this case, we cannot guarantee that the reliability condition will also be satisfied unless the main channel C1 is noiseless, although we believe it can be always satisfied in practice.

Includes members:
  Member "soft decision decoding of reed muller codes recursive lists":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "how to construct polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "polar coding for secure transmission and key agreement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polar codes for broadcast channels")
  Member "nested polar codes for wiretap and relay channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sign bit shaping using polar codes")
  Member "an entropy inequality for q ary random variables and its application to channel polarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the finite length scaling of ternary polar codes")
  Member "strong secrecy for erasure wiretap channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar coding for achieving the capacity of marginal channels in nonbinary input setting")
  Member "secrecy achieving polar coding for binary input memoryless symmetric wire tap channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "greedy merge degrading has optimal power law")
  Member "performance and construction of polar codes on symmetric binary input memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "polar codes and their quantum domain counterparts")

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)