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: "the generalized stochastic likelihood decoder random coding and expurgated bounds"
  Abstract: The likelihood decoder is a stochastic decoder that selects the decoded message at random, using the posterior distribution of the true underlying message given the channel output. In this work, we study a generalized version of this decoder where the posterior is proportional to a general function that depends only on the joint empirical distribution of the output vector and the codeword. This framework allows both mismatched versions and universal (MMI) versions of the likelihood decoder, as well as the corresponding ordinary deterministic decoders, among many others. We provide a direct analysis method that yields the exact random coding exponent (as opposed to separate upper bounds and lower bounds that turn out to be compatible, which were derived earlier by Scarlett et al.). We also extend the result from pure channel coding to combined source and channel coding (random binning followed by random channel coding) with side information (SI) available to the decoder. Finally, returning to pure channel coding, we derive also an expurgated exponent for the stochastic likelihood decoder, which turns out to be at least as tight (and in some cases, strictly so) as the classical expurgated exponent of the maximum likelihood decoder, even though the stochastic likelihood decoder is suboptimal.

Connected through hyperedge HE-1 (induced by source paper "sharp bounds for mutual covering"):
  with members:
    - "exact soft covering exponent"
    - "a unified framework for one shot achievability via the poisson matching lemma"
    - "strong functional representation lemma and applications to coding theorems"
    - "secret key generation with limited interaction"
    - "the likelihood encoder for lossy compression"
    - "on non interactive simulation of joint distributions"
    - "one shot mutual covering lemma and marton s inner bound with a common message"
    - "one shot marton inner bound for classical quantum broadcast channel"

Connected through hyperedge HE-2 (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"
    - "error exponents of typical random codes"
    - "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-3 (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"
    - "error exponents of typical random codes"
    - "correction to the generalized stochastic likelihood decoder random coding and expurgated bounds"
    - "the likelihood encoder for lossy compression"
    - "a technique for deriving one shot achievability results in network information theory"

Connected through hyperedge HE-4 (induced by source paper "error exponents of typical random codes for the colored gaussian channel"):
  with members:
    - "error exponents of typical random codes"
    - "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 "generalized random gilbert varshamov codes"):
  with members:
    - "list decoding random coding exponents and expurgated exponents"
    - "expurgated random coding ensembles exponents refinements and connections"
    - "mismatched decoding error exponents second order rates and saddlepoint approximations"

Connected through hyperedge HE-6 (induced by source paper "false accept false reject trade offs in biometric authentication systems"):
  with members:
    - "the likelihood encoder for lossy compression"
    - "a technique for deriving one shot achievability results in network information theory"

Connected through hyperedge HE-7 (induced by source paper "ensemble performance of biometric authentication systems based on secret key generation"):
  with members:
    - "error exponents of typical random codes"
    - "correction to the generalized stochastic likelihood decoder random coding and expurgated bounds"
    - "the likelihood encoder for lossy compression"
    - "on the reliability function of variable rate slepian wolf coding"
    - "a technique for deriving one shot achievability results in network information theory"

Connected through hyperedge HE-8 (induced by source paper "error exponents of typical random codes"):
  with members:
    - "ensemble performance of biometric authentication systems based on secret key generation"
    - "correction to the generalized stochastic likelihood decoder random coding and expurgated bounds"
    - "the likelihood encoder for lossy compression"
    - "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"

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)