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 "guessing random additive noise decoding with symbol reliability information srgrand"):
  Abstract: In computer communications, discrete data are first channel coded and then modulated into continuous signals for transmission and reception. In a hard detection setting, only demodulated data are provided to the decoder. If soft information on received signal quality is provided, its use can improve decoding accuracy. Incorporating it, however, typically comes at the expense of increased algorithmic complexity. Here we introduce a mechanism to use binarized soft information in the Guessing Random Additive Noise Decoding framework such that decoding accuracy is increased, but computational complexity is decreased. The principle envisages a code-book-independent quantization of soft information where demodulated symbols are additionally indicated to be reliable or unreliable. We introduce two algorithms that incorporate this information, one of which identifies a Maximum Likelihood (ML) decoding and the other either reports an ML decoding or an error. Both are suitable for use with any block-code, and are capacity-achieving. We determine error exponents and asymptotic complexity. They achieve higher rates with lower error probabilities and less algorithmic complexity than their hard detection counterparts. As practical illustrations, we compare performance with majority logic decoding of a Reed-Muller code, with Berlekamp-Massey decoding of a BCH code, and establish performance of Random Linear Codes.

Includes members:
  Member "capacity achieving guessing random additive noise decoding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ordered reliability bits guessing random additive noise decoding")
  Member "a characterization of guesswork on swiftly tilting curves":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fair resource allocation in federated learning")
  Member "on the origin of polar coding":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "polarization adjusted convolutional pac codes fano decoding vs list decoding")
  Member "the random coding bound is tight for the average linear code or lattice":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "polar codes simplicity random codes durability")
  Member "guesswork large deviations and shannon entropy":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mismatched guesswork")
  Member "guessing a password over a wireless channel on the effect of noise non uniformity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity achieving guessing random additive noise decoding")
  Member "channel polarization a method for constructing capacity achieving codes for symmetric binary input memoryless channels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reed muller codes theory and algorithms")
  Member "guessing based on length functions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "capacity achieving guessing random additive noise decoding")

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)