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: "error exponents of typical random codes for the colored gaussian channel"
  Abstract: The error exponent of the typical random code is defined as the asymptotic normalized expectation of the logarithm of the probability of error, as opposed to the traditional definition of the random coding exponent as the normalized logarithm of the expectation of the probability of error with respect to a given ensemble of codes. For a certain ensemble of independent codewords, with a given power spectrum, and a generalized stochastic mismatched decoder, we characterize the error exponent the typical random codes (TRC) for the colored Gaussian channel, with emphasis on the range of low rates, where the TRC error exponent differs in value from the ordinary random coding error exponent. The error exponent formula, which is exponentially tight at some range of low rates, is presented as the maximum of a certain function with respect to one parameter only (in the spirit of Gallager's formulas) in the case of matched decoding, and two parameters in the case of mismatched decoding. Several aspects of the main results are discussed. These include: general properties, a parametric representation, critical rates, phase transitions, optimal input spectrum (water pouring), and comparison to the random coding exponent.

Connected through hyperedge HE-1 (induced by source paper "large deviations behavior of the logarithmic error probability of random codes"):
  with members:
    - "error exponents of typical random trellis codes"
    - "error exponents of typical random codes"
    - "the generalized stochastic likelihood decoder random coding and expurgated bounds"
    - "comparing the bit map and block map decoding thresholds of reed muller codes on bms channels"
    - "channel detection in coded communication"
    - "exact random coding error exponents of optimal bin index decoding"
    - "typicality graphs large deviation analysis"

Connected through hyperedge HE-2 (induced by source paper "a lagrange dual lower bound to the error exponent function of the typical random code"):
  with members:
    - "error exponents of typical random trellis codes"
    - "error exponents of typical random codes"
    - "correction to the generalized stochastic likelihood decoder random coding and expurgated bounds"
    - "the likelihood encoder for lossy compression"
    - "the generalized stochastic likelihood decoder random coding and expurgated bounds"
    - "a technique for deriving one shot achievability results in network information theory"

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)