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 codes with exponentially small error at finite block length"
  Abstract: We show that the entire class of polar codes (up to a natural necessary condition) converge to capacity at block lengths polynomial in the gap to capacity, while simultaneously achieving failure probabilities that are exponentially small in the block length (i.e., decoding fails with probability $\exp(-N^{\Omega(1)})$ for codes of length $N$). Previously this combination was known only for one specific family within the class of polar codes, whereas we establish this whenever the polar code exhibits a condition necessary for any polarization. #R##N#Our results adapt and strengthen a local analysis of polar codes due to the authors with Nakkiran and Rudra [Proc. STOC 2018]. Their analysis related the time-local behavior of a martingale to its global convergence, and this allowed them to prove that the broad class of polar codes converge to capacity at polynomial block lengths. Their analysis easily adapts to show exponentially small failure probabilities, provided the associated martingale, the ``Arikan martingale'', exhibits a corresponding strong local effect. The main contribution of this work is a much stronger local analysis of the Arikan martingale. This leads to the general result claimed above. #R##N#In addition to our general result, we also show, for the first time, polar codes that achieve failure probability $\exp(-N^{\beta})$ for any $\beta < 1$ while converging to capacity at block length polynomial in the gap to capacity. Finally we also show that the ``local'' approach can be combined with any analysis of failure probability of an arbitrary polar code to get essentially the same failure probability while achieving block length polynomial in the gap to capacity.

Connected through hyperedge HE-1 (induced by source paper "sublinear latency for simplified successive cancellation decoding of polar codes"):
  with members:
    - "log logarithmic time pruned polar coding"
    - "polar code moderate deviation recovering the scaling exponent"
    - "partitioned list decoding of polar codes analysis and improvement of finite length performance"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "fast and flexible successive cancellation list decoders for polar codes"
    - "binary linear codes with optimal scaling polar codes with large kernels"
    - "partitioned successive cancellation list decoding of polar codes"
    - "construction of polar codes with sublinear complexity"

Connected through hyperedge HE-2 (induced by source paper "log logarithmic time pruned polar coding"):
  with members:
    - "polar like codes and asymptotic tradeoff among block length code rate and error probability"
    - "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)