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: "polar like codes and asymptotic tradeoff among block length code rate and error probability"
  Abstract: A general framework is proposed that includes polar codes over arbitrary channels with arbitrary kernels. The asymptotic tradeoff among block length $N$, code rate $R$, and error probability $P$ is analyzed. #R##N#Given a tradeoff between $N,P$ and a tradeoff between $N,R$, we return an interpolating tradeoff among $N,R,P$ (Theorem 5). $\def\Capacity{\text{Capacity}}$Quantitatively, if $P=\exp(-N^{\beta^*})$ is possible for some $\beta^*$ and if $R=\Capacity-N^{1/\mu^*}$ is possible for some $1/\mu^*$, then $(P,R)=(\exp(-N^{\beta'}),\Capacity-N^{-1/\mu'})$ is possible for some pair $(\beta',1/\mu')$ determined by $\beta^*$, $1/\mu^*$, and auxiliary information. In fancy words, an error exponent regime tradeoff plus a scaling exponent regime tradeoff implies a moderate deviations regime tradeoff. #R##N#The current world records are: [arXiv:1304.4321][arXiv:1501.02444][arXiv:1806.02405] analyzing Ar{\i}kan's codes over BEC; [arXiv:1706.02458] analyzing Ar{\i}kan's codes over AWGN; and [arXiv:1802.02718][arXiv:1810.04298] analyzing general codes over general channels. An attempt is made to generalize all at once (Section IX). #R##N#As a corollary, a grafted variant of polar coding almost catches up the code rate and error probability of random codes with complexity slightly larger than $N\log N$ over BEC. In particular, $(P,R)=(\exp(-N^{.33}),\Capacity-N^{-.33})$ is possible (Corollary 10). In fact, all points in this triangle are possible $(\beta',1/\mu')$-pairs. $$ #R##N#\require{enclose} #R##N#\def\r{\phantom{\Rule{4em}{1em}{1em}}} #R##N#\enclose{}\r^\llap{(0,1/2)}_\llap{(0,0)} #R##N#\enclose{left,bottom,downdiagonalstrike}\r_\rlap{(1,0)} #R##N#\enclose{}\r $$

Connected through hyperedge HE-1 (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"
    - "explicit polar codes with small scaling exponent"
    - "on the capacity of channels with deletions and states"
    - "polar codes for the deletion channel weak and strong polarization"
    - "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-2 (induced by source paper "log logarithmic time pruned polar coding"):
  with members:
    - "polar codes with exponentially small error at finite block length"
    - "general strong polarization"
    - "polar code moderate deviation recovering the scaling exponent"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed polar codes"
    - "flexible length polar codes through graph based augmentation"
    - "a packing lemma for polar codes"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"

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)