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 "improved bounds on the finite length scaling of polar codes"):
  Abstract: Improved bounds on the blocklength required to communicate over binary-input channels using polar codes, below some given error probability, are derived. For that purpose, an improved bound on the number of non-polarizing channels is obtained. The main result is that the blocklength required to communicate reliably scales at most as $O((I(W)-R)^{-5.77})$ where $R$ is the code rate and $I(W)$ the symmetric capacity of the channel, $W$. The results are then extended to polar lossy source coding at rate $R$ of a source with symmetric distortion-rate function $D(\cdot)$. The blocklength required scales at most as $O((D_N-D(R))^{-5.77})$ where $D_N$ is the actual distortion.

Includes members:
  Member "polar codes for broadcast channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polar codes simplicity random codes durability")
  Member "finite length scaling for polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reed muller codes theory and algorithms")
  Member "achieving marton s region for broadcast channels using polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "achieving the uniform rate region of general multiple access channels by polar coding")
  Member "polar codes speed of polarization and polynomial gap to capacity":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polar codes simplicity random codes durability")
  Member "rate dependent analysis of the asymptotic behavior of channel polarization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polar codes simplicity random codes durability")
  Member "universal bounds on the scaling behavior of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar codes simplicity random codes durability")
  Member "list decoding of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "strong coordination with polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "strong coordination of signals and actions over noisy channels with two sided state information")

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)