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 "near optimal finite length scaling for polar codes over large alphabets"):
  Abstract: For any prime power $q$, Mori and Tanaka introduced a family of $q$-ary polar codes based on $q$~by~$q$ Reed-Solomon polarization kernels. For transmission over a $q$-ary erasure channel, they also derived a closed-form recursion for the erasure probability of each effective channel. In this paper, we use that expression to analyze the finite-length scaling of these codes on the $q$-ary erasure channel with erasure probability $\epsilon\in(0,1)$. Our primary result is that, for any $\gamma>0$ and $\delta>0$, there is a $q_{0}$ such that, for all $q\geq q_{0}$, the fraction of effective channels with erasure rate at most $N^{-\gamma}$ is at least $1-\epsilon-O(N^{-1/2+\delta})$, where $N=q^{n}$ is the blocklength. Since this fraction cannot be larger than $1-\epsilon-O(N^{-1/2})$, this establishes near-optimal finite-length scaling for this family of codes. Our approach can be seen as an extension of a similar analysis for binary polar codes by Hassani, Alishahi, and Urbanke. #R##N#A similar analysis is also considered for $q$-ary polar codes with $m$ by $m$ polarizing matrices. This separates the effect of the alphabet size from the effect of the matrix size. If the polarizing matrix at each stage is drawn independently and uniformly from the set of invertible $m$ by $m$ matrices, then the linear operator associated with the Lyapunov function analysis can be written in closed form. To prove near-optimal scaling for polar codes with fixed $q$ as $m$ increases, however, two technical obstacles remain. Thus, we conclude by stating two concrete mathematical conjectures that, if proven, would imply near-optimal scaling for fixed~$q$.

Includes members:
  Member "binary linear codes with optimal scaling and quasi linear complexity":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the polarizing behavior and scaling exponent of polar codes with product kernels")
  Member "on the finite length scaling of ternary polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptive linear programming decoding of nonbinary linear codes over prime fields")
  Member "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the polarizing behavior and scaling exponent of polar codes with product kernels")
  Member "finite length scaling for polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reed muller codes theory and algorithms")
  Member "binary polarization kernels from code decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "an entropy sumset inequality and polynomially fast convergence to shannon capacity over all alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "countably infinite multilevel source polarization for non stationary erasure distributions")
  Member "polar codes speed of polarization and polynomial gap to capacity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polar codes simplicity random codes durability")
  Member "improved bounds on the finite length scaling of polar codes":
    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)