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 "on the scaling exponent of polar codes for binary input energy harvesting channels"):
  Abstract: This paper investigates the scaling exponent of polar codes for binary-input energy-harvesting (EH) channels with infinite-capacity batteries. The EH process is characterized by a sequence of independent and identically distributed random variables with finite variances. The scaling exponent    $\mu $    of polar codes for a binary-input memoryless channel (BMC)    $q_{Y|X}$    with capacity    $\mathrm {C}(q_{Y|X})$    characterizes the closest gap between the capacity and the non-asymptotic achievable rates in the following way. For a fixed average error probability    $\varepsilon \in (0, 1)$   , the closest gap between the capacity    $\mathrm {C}(q_{Y|X})$    and a non-asymptotic achievable rate    $R_{n}$    for a length-   $n$    polar code scales as    $n^{-1/\mu }$   , i.e.,    $\min \{|\mathrm {C}(q_{Y|X})-R_{n}|\} = \Theta (n^{-1/\mu })$   . It has been shown that the scaling exponent    $\mu $    for any binary-input memoryless symmetric channel with    $\mathrm {C}(q_{Y|X})\in (0,1)$    lies between 3.579 and 4.714, where the upper bound 4.714 was shown by an explicit construction of polar codes. Our main result shows that 4.714 remains to be a valid upper bound on the scaling exponent for any binary-input EH channel, i.e., a BMC subject to additional EH constraints. Our result thus implies that the EH constraints do not worsen the rate of convergence to capacity if polar codes are employed. An auxiliary contribution of this paper is that the upper bound on    $\mu $    holds for binary-input memoryless asymmetric channels.

Includes members:
  Member "non asymptotic achievable rates for energy harvesting channels using save and transmit":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "second order and moderate deviation analysis of a block fading channel with deterministic and energy harvesting power constraints")
  Member "energy harvesting wireless communications a review of recent advances":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analysis of d2d communication with rf energy harvesting and interference management")
  Member "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "reed muller codes theory and algorithms")
  Member "near optimal energy control and approximate capacity of energy harvesting communication":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a maximin optimal online power control policy for energy harvesting communications")
  Member "how to achieve the capacity of asymmetric channels":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explicit codes for the wiretap channel a unified design framework")
  Member "improved bounds on the finite length scaling of polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "polar codes simplicity random codes durability")
  Member "achieving the capacity of any dmc using only polar codes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "polar codes simplicity random codes durability")

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)