human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "second order rate region of constant composition codes for the multiple access channel"):
  Abstract: This paper studies the second-order asymptotics of coding rates for the discrete memoryless multiple-access channel with a fixed target error probability. Using constant-composition random coding, coded time-sharing, and a variant of Hoeffding's combinatorial central limit theorem, an inner bound on the set of locally achievable second-order coding rates is given for each point on the boundary of the capacity region. It is shown that the inner bound for constant-composition random coding includes that recovered by i.i.d. random coding, and that the inclusion may be strict. The inner bound is extended to the Gaussian multiple-access channel via an increasingly fine quantization of the inputs.

Includes members:
  Member "second order asymptotics for the gaussian mac with degraded message sets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "second order region for gray wyner network")
  Member "a case where interference does not affect the channel dispersion":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "second order region for gray wyner network")
  Member "on the dispersions of three network information theory problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "a finite blocklength perspective on gaussian multi access channels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a proof of the strong converse theorem for gaussian multiple access channels")
  Member "moderate deviations in channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal resolution of change point detection with empirically observed statistics and erasures")
  Member "second order slepian wolf coding theorems for non mixed and mixed sources":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless source coding in the point to point multiple access and random access scenarios")
  Member "information spectrum approach to second order coding rate in channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "finite sample analysis of image registration")

Question: Please tell me which class the source paper 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)