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 "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.

Includes members:
  Member "on the scaling exponent of polar codes for binary input energy harvesting channels":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "polar codes simplicity random codes durability")
  Member "achieving the uniform rate region of general multiple access channels by polar coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "channel coding at low capacity")
  Member "wasserstein continuity of entropy and outer bounds for interference channels":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a lower bound on the expected distortion of joint source channel coding")
  Member "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "reed muller codes theory and algorithms")
  Member "improved bounds on the finite length scaling of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "polar codes simplicity random codes durability")
  Member "polar codes for the two user multiple access channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polar codes simplicity random codes durability")
  Member "moderate deviations in channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "optimal resolution of change point detection with empirically observed statistics and erasures")

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)