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: "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
  Abstract: This paper investigates polar codes for the additive white Gaussian noise (AWGN) channel. The scaling exponent    μ    of polar codes for a memoryless channel     q  Y | X      with capacity     I (  q  Y | X   )     characterizes the closest gap between the capacity and non-asymptotic achievable rates as follows: For a fixed     e ∈ ( 0 , 1 )    , the gap between the capacity     I (  q  Y | X   )     and the maximum non-asymptotic rate     R n *     achieved by a length-n polar code with average error probability    e    scales as     n  - 1 / μ     , i.e.,     I  (  q  Y | X   )  -  R n *  = Θ  (  n  - 1 / μ   )     . It is well known that the scaling exponent    μ    for any binary-input memoryless channel (BMC) with     I  (  q  Y | X   )  ∈  ( 0 , 1 )      is bounded above by     4 . 714    . Our main result shows that     4 . 714     remains a valid upper bound on the scaling exponent for the AWGN channel. Our proof technique involves the following two ideas: (i) The capacity of the AWGN channel can be achieved within a gap of     O (  n  - 1 / μ     log n   )     by using an input alphabet consisting of n constellations and restricting the input distribution to be uniform; (ii) The capacity of a multiple access channel (MAC) with an input alphabet consisting of n constellations can be achieved within a gap of     O (  n  - 1 / μ   log n )     by using a superposition of     log n     binary-input polar codes. In addition, we investigate the performance of polar codes in the moderate deviations regime where both the gap to capacity and the error probability vanish as n grows. An explicit construction of polar codes is proposed to obey a certain tradeoff between the gap to capacity and the decay rate of the error probability for the AWGN channel.

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"
    - "polar like codes and asymptotic tradeoff among block length code rate and error probability"
    - "relaxed polar codes"
    - "exact random coding secrecy exponents for the wiretap channel"

Connected through hyperedge HE-2 (induced by source paper "sublinear latency for simplified successive cancellation decoding of polar codes"):
  with members:
    - "log logarithmic time pruned polar coding"
    - "polar codes with exponentially small error at finite block length"
    - "polar code moderate deviation recovering the scaling exponent"
    - "partitioned list decoding of polar codes analysis and improvement of finite length performance"
    - "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-3 (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"
    - "polar codes with exponentially small error at finite block length"
    - "general strong polarization"
    - "polar code moderate deviation recovering the scaling exponent"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "polar like codes and asymptotic tradeoff among block length code rate and error probability"):
  with members:
    - "performance bounds of concatenated polar coding schemes"
    - "relaxed polar codes"
    - "minimum distance based construction of multi kernel polar codes"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "a note on some inequalities used in channel polarization and polar coding"
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "multi kernel construction of polar codes"
    - "flexible length polar codes through graph based augmentation"

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)