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 trellis codes"
  Abstract: In continuation to an earlier work, where error exponents of typical random codes were studied in the context of general block coding, with no underlying structure, here we carry out a parallel study on typical random, time-varying trellis codes for general discrete memoryless channels, focusing on a certain range of low rates. By analyzing an upper bound to the error probability of the typical random trellis code, using the method of types, we first derive a Csiszar-style error exponent formula (with respect to the constraint length), which allows to easily identify and characterize properties of good codes and dominant error events. We also derive a Gallager-style form of this error exponent, which turns out to be related to the expurgated error exponent. The main result is further extended to channels with memory and mismatch.

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 codes for the colored gaussian channel"
    - "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 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"
    - "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 exponents for asynchronous multiple access channels controlled asynchronism may outperform synchronism"):
  with members:
    - "shifted coded slotted aloha"
    - "distributed structure joint expurgation for the multiple access channel"
    - "universal random access error exponents for codebooks of different word lengths"
    - "random access and source channel coding error exponents for multiple access channels"
    - "error exponent for multiple access channels lower bounds"

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)