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: "correction to the generalized stochastic likelihood decoder random coding and expurgated bounds"
  Abstract: This purpose of this letter is to handle a gap that was found in the proof of Theorem 2 in the paper "The generalized stochastic likelihood decoder: random coding and expurgated bounds."

Connected through hyperedge HE-1 (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"
    - "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-2 (induced by source paper "ensemble performance of biometric authentication systems based on secret key generation"):
  with members:
    - "error exponents of typical random codes"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "error exponents of typical random codes"):
  with members:
    - "ensemble performance of biometric authentication systems based on secret key generation"
    - "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"
    - "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)