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: "universal decoding for arbitrary channels relative to a given class of decoding metrics"
  Abstract: We consider the problem of universal decoding for arbitrary, finite-alphabet unknown channels in the random coding regime. For a given random coding distribution and a given class of metric decoders, we propose a generic universal decoder whose average error probability is, within a subexponential multiplicative factor, no larger than that of the best decoder within this class of decoders. Since the optimum, maximum likelihood (ML) decoder of the underlying channel is not necessarily assumed to belong to the given class of decoders, this setting suggests a common generalized framework for: 1) mismatched decoding, 2) universal decoding for a given family of channels, and 3) universal coding and decoding for deterministic channels using the individual sequence approach. The proof of our universality result is fairly simple, and it is demonstrated how some earlier results on universal decoding are obtained as special cases. We also demonstrate how our method extends to more complicated scenarios, like incorporation of noiseless feedback, the multiple access channel, and continuous alphabet channels.

Connected through hyperedge HE-1 (induced by source paper "on the bee identification error exponent with absentee bees"):
  with members:
    - "capacity results for the noisy shuffling channel"
    - "on identifying a massive number of distributions"
    - "codes in the space of multisets coding for permutation channels with impairments"
    - "fundamental limits of dna storage systems"
    - "distance distribution of binary codes and the error probability of decoding"

Connected through hyperedge HE-2 (induced by source paper "gaussian intersymbol interference channels with mismatch"):
  with members:
    - "on the asymptotic equivalence of circulant and toeplitz matrices"
    - "on the evaluation of the polyanskiy poor verdu converse bound for finite block length coding in awgn"
    - "random coding error exponents for the two user interference channel"
    - "universal decoding for gaussian intersymbol interference channels"
    - "erasure list exponents for slepian wolf decoding"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"
    - "a general formula for the mismatch capacity"
    - "analysis of mismatched estimation errors using gradients of partition functions"

Connected through hyperedge HE-3 (induced by source paper "universal decoding using a noisy codebook"):
  with members:
    - "reliability of universal decoding based on vector quantized codewords"
    - "communication over individual channels a general framework"
    - "communication over individual channels"

Connected through hyperedge HE-4 (induced by source paper "reliability of universal decoding based on vector quantized codewords"):
  with members:
    - "universal decoding using a noisy codebook"

Connected through hyperedge HE-5 (induced by source paper "universal decoding for source channel coding with side information"):
  with members:
    - "exact random coding error exponents of optimal bin index decoding"
    - "erasure list exponents for slepian wolf decoding"
    - "multiuser random coding techniques for mismatched decoding"
    - "communication over individual channels a general framework"
    - "the porosity of additive noise sequences"
    - "communication over individual channels"
    - "universal communication part i modulo additive channels"
    - "relations between random coding exponents and the statistical physics of random codes"

Connected through hyperedge HE-6 (induced by source paper "a universal decoder relative to a given family of metrics"):
  with members:
    - "universal decoding for gaussian intersymbol interference channels"
    - "communication over individual channels a general framework"
    - "universal communication over arbitrarily varying channels"
    - "universal communication part i modulo additive channels"
    - "blind decoding of linear gaussian channels with isi capacity error exponent universality"

Connected through hyperedge HE-7 (induced by source paper "universal decoding for gaussian intersymbol interference channels"):
  with members:
    - "asymptotic mmse analysis under sparse representation modeling"
    - "analysis of mismatched estimation errors using gradients of partition functions"
    - "communication over individual channels a general framework"
    - "the porosity of additive noise sequences"
    - "universal communication part i modulo additive channels"

Connected through hyperedge HE-8 (induced by source paper "exponential error bounds on parameter modulation estimation for discrete memoryless channels"):
  with members:
    - "on optimum parameter modulation estimation from a large deviations perspective"
    - "data processing inequalities based on a certain structured class of information measures with application to estimation theory"
    - "a lower bound on the bayesian mse based on the optimal bias function"

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)