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: "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.

Connected through hyperedge HE-1 (induced by source paper "ordered reliability bits guessing random additive noise decoding"):
  with members:
    - "guessing random additive noise decoding with symbol reliability information srgrand"
    - "guessing random additive noise decoding with soft detection symbol reliability information sgrand"
    - "fast and flexible software polar list decoders"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"

Connected through hyperedge HE-2 (induced by source paper "soft maximum likelihood decoding using grand"):
  with members:
    - "design of polar codes in 5g new radio"
    - "fast list decoders for polar codes"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar codes"

Connected through hyperedge HE-3 (induced by source paper "guessing random additive noise decoding with symbol reliability information srgrand"):
  with members:
    - "a characterization of guesswork on swiftly tilting curves"
    - "on the origin of polar coding"
    - "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:
    - "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"

Connected through hyperedge HE-5 (induced by source paper "5g nr ca polar maximum likelihood decoding by grand"):
  with members:
    - "guessing random additive noise decoding with soft detection symbol reliability information sgrand"
    - "llr based successive cancellation list decoding of polar codes"
    - "list decoding of polar 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)