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"
  Abstract: We define the error exponent of the typical random code as the long-block limit of the negative normalized expectation of the logarithm of the error probability of the random code, as opposed to the traditional random coding error exponent, which is the limit of the negative normalized logarithm of the expectation of the error probability. For the ensemble of uniformly randomly drawn fixed composition codes, we provide exact error exponents of typical random codes for a general discrete memoryless channel (DMC) and a wide class of (stochastic) decoders, collectively referred to as the generalized likelihood decoder (GLD). This ensemble of fixed composition codes is shown to be no worse than any other ensemble of independent codewords that are drawn under a permutation--invariant distribution (e.g., i.i.d. codewords). We also present relationships between the error exponent of the typical random code and the ordinary random coding error exponent, as well as the expurgated exponent for the GLD. Finally, we demonstrate that our analysis technique is applicable also to more general communication scenarios, such as list decoding (for fixed-size lists) as well as decoding with an erasure/list option in Forney's sense.

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 for the colored gaussian channel"
    - "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 for the colored gaussian channel"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "error exponent bounds for the bee identification problem"):
  with members:
    - "capacity results for the noisy shuffling channel"
    - "the strongly asynchronous massive access 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"

Connected through hyperedge HE-4 (induced by source paper "error exponents of typical random codes for the colored gaussian channel"):
  with members:
    - "the generalized stochastic likelihood decoder random coding and expurgated bounds"
    - "a technique for deriving one shot achievability results in network information theory"
    - "how could the replica method improve accuracy of performance assessment of channel coding"

Connected through hyperedge HE-5 (induced by source paper "ensemble performance of biometric authentication systems based on secret key generation"):
  with members:
    - "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"
    - "on the reliability function of variable rate slepian wolf coding"
    - "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)