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: "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.

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"
    - "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"
    - "relaxed polar codes"

Connected through hyperedge HE-2 (induced by source paper "scaling exponent and moderate deviations asymptotics of polar codes for the awgn channel"):
  with members:
    - "achieving the uniform rate region of general multiple access channels by polar coding"
    - "wasserstein continuity of entropy and outer bounds for interference channels"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "improved bounds on the finite length scaling of polar codes"
    - "polar codes for the two user multiple access channel"
    - "moderate deviations in channel coding"
    - "achieving the capacity of any dmc using only polar codes"

Connected through hyperedge HE-3 (induced by source paper "channel capacity of polar coding with a given polar mismatched successive cancellation decoder"):
  with members:
    - "a simple proof of fast polarization"
    - "a split reduced successive cancellation list decoder for polar codes"
    - "a low latency list successive cancellation decoding implementation for polar codes"
    - "a high throughput list decoder architecture for polar codes"
    - "fast list decoders for polar codes"
    - "symbol decision successive cancellation list decoder for polar codes"
    - "polar coding for processes with memory"
    - "erasure schemes using generalized polar codes zero undetected error capacity and performance trade offs"

Connected through hyperedge HE-4 (induced by source paper "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"):
  with members:
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "finite length scaling for polar codes"
    - "from polar to reed muller codes a technique to improve the finite length performance"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "improved bounds on the finite length scaling of polar codes"
    - "scaling exponent of list decoders with applications to polar codes"
    - "rate dependent analysis of the asymptotic behavior of channel polarization"
    - "list decoding of polar codes"

Connected through hyperedge HE-5 (induced by source paper "how to achieve the capacity of asymmetric channels"):
  with members:
    - "threshold saturation for nonbinary sc ldpc codes on the binary erasure channel"
    - "construction of polar codes with sublinear complexity"
    - "polar codes for broadcast channels"
    - "a scaling law to predict the finite length performance of spatially coupled ldpc codes"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"
    - "achieving marton s region for broadcast channels using polar codes"
    - "threshold analysis of non binary spatially coupled ldpc codes with windowed decoding"

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)