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 "binary linear codes with optimal scaling polar codes with large kernels"):
  Abstract: We prove that, at least for the binary erasure channel (BEC), the polar-coding paradigm gives rise to codes that not only approach the Shannon limit but do so under the best possible scaling of their block length as a function of the gap to capacity. This result exhibits the first known family of binary codes that attain both optimal scaling and quasi-linear complexity of construction, encoding and decoding. Specifically, for any fixed $\delta > 0$, we exhibit binary linear codes that ensure reliable communication at rates within $\varepsilon > 0$ of capacity with block length $n=O(1/\varepsilon^{2+\delta})$, construction complexity $\Theta(n)$, and encoding/decoding complexity $\Theta(n\log n)$. #R##N#Our proof is based on the construction and analysis of binary polar codes with large kernels. It was recently shown that, for all binary-input symmetric memoryless channels, conventional polar codes (based on a $2\times 2$ kernel) allow reliable communication at rates within $\varepsilon > 0$ of the Shannon capacity with block length, construction, encoding and decoding complexity all bounded by a polynomial in $1/\varepsilon$. In particular, this means that the block length $n$ scales as $O(1/\varepsilon^{\mu})$, where the constant $\mu$ is called the scaling exponent. It is furthermore known that the optimal scaling exponent is $\mu=2$, and it is achieved by random linear codes. However, for general channels, the decoding complexity of random linear codes is exponential in the block length. As far as conventional polar codes, their scaling exponent depends on the channel, and for the binary erasure channel it is given by $\mu=3.63$. #R##N#Our main contribution is a rigorous proof of the following result: there exist $\ell\times\ell$ binary kernels, such that polar codes constructed from these kernels achieve scaling exponent $\mu(\ell)$ that tends to the optimal value of $2$ as $\ell$ grows.

Includes members:
  Member "achieving the uniform rate region of general multiple access channels by polar coding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "channel coding at low capacity")
  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 "construction of polar codes with sublinear complexity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "decoder tailored polar code design using the genetic algorithm")
  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 "performance limits and practical decoding of interleaved reed solomon polar concatenated codes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "threshold secure coding with shared key")
  Member "polar codes speed of polarization and polynomial gap to capacity":
    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)