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 "from polar to reed muller codes a technique to improve the finite length performance"):
  Abstract: We explore the relationship between polar and RM codes and we describe a coding scheme which improves upon the performance of the standard polar code at practical block lengths. Our starting point is the experimental observation that RM codes have a smaller error probability than polar codes under MAP decoding. This motivates us to introduce a family of codes that "interpolates" between RM and polar codes, call this family ${\mathcal C}_{\rm inter} = \{C_{\alpha} : \alpha \in [0, 1]\}$, where $C_{\alpha} \big |_{\alpha = 1}$ is the original polar code, and $C_{\alpha} \big |_{\alpha = 0}$ is an RM code. Based on numerical observations, we remark that the error probability under MAP decoding is an increasing function of $\alpha$. MAP decoding has in general exponential complexity, but empirically the performance of polar codes at finite block lengths is boosted by moving along the family ${\mathcal C}_{\rm inter}$ even under low-complexity decoding schemes such as, for instance, belief propagation or successive cancellation list decoder. We demonstrate the performance gain via numerical simulations for transmission over the erasure channel as well as the Gaussian channel.

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 "recursive decoding and its performance for low rate reed muller codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reed muller codes theory and algorithms")
  Member "finite length scaling for polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "reed muller codes theory and algorithms")
  Member "a rm polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polarization adjusted convolutional pac codes fano decoding vs list decoding")
  Member "improved bounds on the finite length scaling of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polar codes simplicity random codes durability")
  Member "on the correlation between polarized becs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar code moderate deviation recovering the scaling exponent")
  Member "scaling exponent of list decoders with applications to polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "rate dependent analysis of the asymptotic behavior of channel polarization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "polar codes simplicity random codes durability")

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)