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 "capacity achieving guessing random additive noise decoding"):
  Abstract: We introduce a new algorithm for realizing maximum likelihood (ML) decoding for arbitrary codebooks in discrete channels with or without memory, in which the receiver rank-orders noise sequences from most likely to least likely. Subtracting noise from the received signal in that order, the first instance that results in a member of the codebook is the ML decoding. We name this algorithm GRAND for Guessing Random Additive Noise Decoding. We establish that GRAND is capacity-achieving when used with random codebooks. For rates below capacity, we identify error exponents, and for rates beyond capacity, we identify success exponents. We determine the scheme’s complexity in terms of the number of computations that the receiver performs. For rates beyond capacity, this reveals thresholds for the number of guesses by which, if a member of the codebook is identified, that it is likely to be the transmitted code word. We introduce an approximate ML decoding scheme where the receiver abandons the search after a fixed number of queries, an approach we dub GRANDAB, for GRAND with ABandonment. While not an ML decoder, we establish that the algorithm GRANDAB is also capacity-achieving for an appropriate choice of abandonment threshold, and characterize its complexity, error, and success exponents. Worked examples are presented for Markovian noise that indicate these decoding schemes substantially outperform the brute force decoding approach.

Includes members:
  Member "multi user guesswork and brute force security":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "mismatched guesswork")
  Member "on the origin of polar coding":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "polar codes simplicity random codes durability")
  Member "guesswork large deviations and shannon entropy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "mismatched guesswork")
  Member "brute force searching the typical set and guesswork":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a characterization of guesswork on swiftly tilting curves")
  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 "guessing random additive noise decoding with symbol reliability information srgrand")
  Member "frames in outdoor 802 11 wlans provide a hybrid binary symmetric packet erasure channel":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi destination aggregation with binary symmetric broadcast channel based coding in 802 11 wlans")
  Member "guessing revisited a large deviations approach":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "practical estimation of renyi entropy")

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)