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 origin of polar coding"
  Abstract: Polar coding was conceived originally as a technique for boosting the cutoff rate of sequential decoding, along the lines of earlier schemes of Pinsker and Massey. The key idea in boosting the cutoff rate is to take a vector channel (either given or artificially built), split it into multiple correlated subchannels, and employ a separate sequential decoder on each subchannel. Polar coding was originally designed to be a low-complexity recursive channel combining and splitting operation of this type, to be used as the inner code in a concatenated scheme with outer convolutional coding and sequential decoding. However, the polar inner code turned out to be so effective that no outer code was actually needed to achieve the original aim of boosting the cutoff rate to channel capacity. This paper explains the cutoff rate considerations that motivated the development of polar coding.

Connected through hyperedge HE-1 (induced by source paper "polarization adjusted convolutional pac codes fano decoding vs list decoding"):
  with members:
    - "on pre transformed polar codes"
    - "from sequential decoding to channel polarization and back again"
    - "parity check polar coding for 5g and beyond"
    - "a randomized construction of polar subcodes"
    - "llr based successive cancellation list decoding of polar codes"
    - "a rm polar codes"
    - "a low complexity improved successive cancellation decoder for polar codes"
    - "list decoding of polar codes"

Connected through hyperedge HE-2 (induced by source paper "capacity achieving guessing random additive noise decoding"):
  with members:
    - "multi user guesswork and brute force security"
    - "the random coding bound is tight for the average linear code or lattice"
    - "guesswork large deviations and shannon entropy"
    - "brute force searching the typical set and guesswork"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "frames in outdoor 802 11 wlans provide a hybrid binary symmetric packet erasure channel"
    - "guessing revisited a large deviations approach"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-3 (induced by source paper "guessing random additive noise decoding with symbol reliability information srgrand"):
  with members:
    - "capacity achieving guessing random additive noise decoding"
    - "a characterization of guesswork on swiftly tilting curves"
    - "the random coding bound is tight for the average linear code or lattice"
    - "guesswork large deviations and shannon entropy"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "guessing based on length functions"
    - "iterative algebraic soft decision list decoding of reed solomon codes"

Connected through hyperedge HE-4 (induced by source paper "guessing random additive noise decoding with soft detection symbol reliability information sgrand"):
  with members:
    - "capacity achieving guessing random additive noise decoding"
    - "a characterization of guesswork on swiftly tilting curves"
    - "guesswork large deviations and shannon entropy"
    - "guessing a password over a wireless channel on the effect of noise non uniformity"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"
    - "guessing based on length functions"
    - "iterative algebraic soft decision list decoding of reed solomon codes"

Connected through hyperedge HE-5 (induced by source paper "from sequential decoding to channel polarization and back again"):
  with members:
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "list decoding of polar codes"
    - "on the construction of polar codes"
    - "how to construct polar codes"
    - "on the rate of channel polarization"

Connected through hyperedge HE-6 (induced by source paper "log logarithmic time pruned polar coding on binary erasure channels"):
  with members:
    - "relaxed polar codes"
    - "flexible length polar codes through graph based augmentation"
    - "a packing lemma for polar codes"
    - "fast polar decoders algorithm and implementation"
    - "polar codes speed of polarization and polynomial gap to capacity"
    - "spatially coupled ensembles universally achieve capacity under belief propagation"
    - "moderate deviations in channel coding"
    - "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels"

Connected through hyperedge HE-7 (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"
    - "on the scaling exponent of polar codes for binary input energy harvesting channels"
    - "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"

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)