human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "explicit polar codes with small scaling exponent"
  Abstract: Herein, we focus on explicit constructions of $\ell\times\ell$ binary kernels with small scaling exponent for $\ell \le 64$. In particular, we exhibit a sequence of binary linear codes that approaches capacity on the BEC with quasi-linear complexity and scaling exponent $\mu < 3$. To the best of our knowledge, such a sequence of codes was not previously known to exist. The principal challenges in establishing our results are twofold: how to construct such kernels and how to evaluate their scaling exponent. #R##N#In a single polarization step, an $\ell\times\ell$ kernel $K_\ell$ transforms an underlying BEC into $\ell$ bit-channels $W_1,W_2,\ldots,W_\ell$. The erasure probabilities of $W_1,W_2,\ldots,W_\ell$, known as the polarization behavior of $K_\ell$, determine the resulting scaling exponent $\mu(K_\ell)$. We first introduce a class of self-dual binary kernels and prove that their polarization behavior satisfies a strong symmetry property. This reduces the problem of constructing $K_\ell$ to that of producing a certain nested chain of only $\ell/2$ self-orthogonal codes. We use nested cyclic codes, whose distance is as high as possible subject to the orthogonality constraint, to construct the kernels $K_{32}$ and $K_{64}$. In order to evaluate the polarization behavior of $K_{32}$ and $K_{64}$, two alternative trellis representations (which may be of independent interest) are proposed. Using the resulting trellises, we show that $\mu(K_{32})=3.122$ and explicitly compute over half of the polarization behavior coefficients for $K_{64}$, at which point the complexity becomes prohibitive. To complete the computation, we introduce a Monte-Carlo interpolation method, which produces the estimate $\mu(K_{64})\simeq 2.87$. We augment this estimate with a rigorous proof that $\mu(K_{64})<2.97$.

Connected through hyperedge HE-1 (induced by source paper "on the polarizing behavior and scaling exponent of polar codes with product kernels"):
  with members:
    - "minimum distance based construction of multi kernel polar codes"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "list decoding of polar codes"
    - "polar codes characterization of exponent bounds and constructions"

Connected through hyperedge HE-2 (induced by source paper "polar codes simplicity random codes durability"):
  with members:
    - "reed muller codes polarize"
    - "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"
    - "on the capacity of channels with deletions and states"
    - "polar codes for the deletion channel weak and strong polarization"
    - "polar like codes and asymptotic tradeoff among block length code rate and error probability"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed polar codes"
    - "exact random coding secrecy exponents for the wiretap channel"

Connected through hyperedge HE-3 (induced by source paper "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"):
  with members:
    - "general strong polarization"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "multi kernel construction of polar codes"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "polar codes speed of polarization and polynomial gap to capacity"

Connected through hyperedge HE-4 (induced by source paper "on the scaling exponent of polar codes with product kernels"):
  with members:
    - "minimum distance based construction of multi kernel polar codes"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "finite length scaling for polar codes"
    - "list decoding of polar codes"
    - "polar codes characterization of exponent bounds and constructions"

Connected through hyperedge HE-5 (induced by source paper "the penalty in scaling exponent for polar codes is analytically approximated by the golden ratio"):
  with members:
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"

Question: Please tell me which class the center node 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)