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: "the random coding bound is tight for the average linear code or lattice"
  Abstract: In 1973, Gallager proved that the random-coding bound is exponentially tight for the random code ensemble at all rates, even below expurgation. This result explained that the random-coding exponent does not achieve the expurgation exponent due to the properties of the random ensemble, irrespective of the utilized bounding technique. It has been conjectured that this same behavior holds true for a random ensemble of linear codes. This conjecture is proved in this paper. Additionally, it is shown that this property extends to Poltyrev's random-coding exponent for a random ensemble of lattices.

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 "ar i kan meets shannon polar codes with near optimal convergence to channel capacity"):
  with members:
    - "explicit polar codes with small scaling exponent"
    - "general strong polarization"
    - "binary linear codes with optimal scaling and quasi linear complexity"
    - "multi kernel polar codes proof of polarization and error exponents"
    - "near optimal finite length scaling for polar codes over large alphabets"
    - "multi kernel construction of polar codes"
    - "unified scaling of polar codes error exponent scaling exponent moderate deviations and error floors"
    - "finite length scaling for polar codes"

Connected through hyperedge HE-3 (induced by source paper "capacity achieving guessing random additive noise decoding"):
  with members:
    - "multi user guesswork and brute force security"
    - "on the origin of polar coding"
    - "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-4 (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"
    - "on the origin of polar coding"
    - "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"

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)