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 "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"):
  Abstract: Consider the transmission of a polar code of block length $N$ and rate $R$ over a binary memoryless symmetric channel $W$ and let $P_e$ be the block error probability under successive cancellation decoding. In this paper, we develop new bounds that characterize the relationship of the parameters $R$, $N$, $P_e$, and the quality of the channel $W$ quantified by its capacity $I(W)$ and its Bhattacharyya parameter $Z(W)$. #R##N#In previous work, two main regimes were studied. In the error exponent regime, the channel $W$ and the rate $R<I(W)$ are fixed, and it was proved that the error probability $P_e$ scales roughly as $2^{-\sqrt{N}}$. In the scaling exponent approach, the channel $W$ and the error probability $P_e$ are fixed and it was proved that the gap to capacity $I(W)-R$ scales as $N^{-1/\mu}$. Here, $\mu$ is called scaling exponent and this scaling exponent depends on the channel $W$. A heuristic computation for the binary erasure channel (BEC) gives $\mu=3.627$ and it was shown that, for any channel $W$, $3.579 \le \mu \le 5.702$. #R##N#Our contributions are as follows. First, we provide the tighter upper bound $\mu \le 4.714$ valid for any $W$. With the same technique, we obtain $\mu \le 3.639$ for the case of the BEC, which approaches very closely its heuristically derived value. Second, we develop a trade-off between the gap to capacity $I(W)-R$ and the error probability $P_e$ as functions of the block length $N$. In other words, we consider a moderate deviations regime in which we study how fast both quantities, as functions of the block length $N$, simultaneously go to $0$. Third, we prove that polar codes are not affected by error floors. To do so, we fix a polar code of block length $N$ and rate $R$. Then, we vary the channel $W$ and we show that the error probability $P_e$ scales as the Bhattacharyya parameter $Z(W)$ raised to a power that scales roughly like $\sqrt{N}$.

Includes members:
  Member "on the scaling exponent of polar codes for binary input energy harvesting channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polar codes simplicity random codes durability")
  Member "near optimal finite length scaling for polar codes over large alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "polar codes simplicity random codes durability")
  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 "from polar to reed muller codes a technique to improve the finite length performance":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "polar codes speed of polarization and polynomial gap to capacity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "polar codes simplicity random codes durability")
  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)