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 "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"):
  Abstract: Let $W$ be a binary-input memoryless symmetric (BMS) channel with Shannon capacity $I(W)$ and fix any $\alpha > 0$. We construct, for any sufficiently small $\delta > 0$, binary linear codes of block length $O(1/\delta^{2+\alpha})$ and rate $I(W)-\delta$ that enable reliable communication on $W$ with quasi-linear time encoding and decoding. Shannon's noisy coding theorem established the existence of such codes (without efficient constructions or decoding) with block length $O(1/\delta^2)$. This quadratic dependence on the gap $\delta$ to capacity is known to be best possible. Our result thus yields a constructive version of Shannon's theorem with near-optimal convergence to capacity as a function of the block length. This resolves a central theoretical challenge associated with the attainment of Shannon capacity. Previously such a result was only known for the erasure channel. #R##N#Our codes are a variant of Arikan's polar codes based on multiple carefully constructed local kernels, one for each intermediate channel that arises in the decoding. A crucial ingredient in the analysis is a strong converse of the noisy coding theorem when communicating using random linear codes on arbitrary BMS channels. Our converse theorem shows extreme unpredictability of even a single message bit for random coding at rates slightly above capacity.

Includes members:
  Member "explicit polar codes with small scaling exponent":
    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 "general strong polarization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "reed muller codes theory and algorithms")
  Member "binary linear codes with optimal scaling and quasi linear complexity":
    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 "multi kernel polar codes proof of polarization and error exponents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "near optimal finite length scaling for polar codes over large alphabets":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "polar codes simplicity random codes durability")
  Member "multi kernel construction of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar codes and their quantum domain counterparts")
  Member "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "reed muller codes theory and algorithms")

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)