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 "capacity approaching polar codes with long codewords and successive cancellation decoding based on improved gaussian approximation"):
  Abstract: This paper focuses on an improved Gaussian approximation~(GA) based construction of polar codes with successive cancellation~(SC) decoding over an additive white Gaussian noise~(AWGN) channel. It has been theoretically proven by Arikan that polar codes with low-complexity SC decoder can approach the channel capacity of an arbitrary symmetric binary-input discrete memoryless channel, provided that the code length is chosen large enough. Nevertheless, how to construct such codes over an AWGN channel with low computational effort has been an open problem. The conventional GA is a powerful and low complexity technique, but due to numerical issues, it can fail to accurately trace the evolution of the mean log likelihood ratio~(LLR) value as the code length increases. In this work, an improved GA approach is proposed where, by judiciously defining the functions associated with the GA, an accurate trace of mean LLR evolution becomes feasible. With this improved GA, through theoretical analysis and simulations with code lengths up to $N=2^{18}$, we explicitly demonstrate that various code-rate polar codes with long codeword and capacity approaching behavior can be easily constructed.

Includes members:
  Member "decoder tailored polar code design using the genetic algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "a comparative study of polar code constructions for the awgn channel":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "does gaussian approximation work well for the long length polar code construction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sphere constraint based enumeration methods to analyze the minimum weight distribution of polar codes")
  Member "universal polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "list decoding of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "on finite length performance of polar codes stopping sets error floor and concatenated design":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "partially permuted multi trellis belief propagation for polar codes")
  Member "how to construct polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "reed muller codes theory and algorithms")

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)