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 "the dispersion of the gauss markov source"):
  Abstract: The Gauss–Markov source produces    $U_{i} = aU_{i-1} + Z_{i}$    for    $i\geq 1$   , where    $U_{0} = 0$   ,    $|a|   and    $Z_{i}\sim \mathcal {N}(0, \sigma ^{2})$    are i.i.d. Gaussian random variables. We consider lossy compression of a block of    $n$    samples of the Gauss–Markov source under squared error distortion. We obtain the Gaussian approximation for the Gauss–Markov source with excess-distortion criterion for any distortion    $d>0$   , and we show that the dispersion has a reverse waterfilling representation. This is the  first  finite blocklength result for lossy compression of  sources with memory . We prove that the finite blocklength rate-distortion function    $R(n,d,\epsilon)$    approaches the rate-distortion function    $\mathbb {R}(d)$    as    $R(n,d,\epsilon) = \mathbb {R}(d) + \sqrt {\frac {V(d)}{n}}Q^{-1}(\epsilon) + o\left ({\frac {1}{\sqrt {n}}}\right)$   , where    $V(d)$    is the dispersion,    $\epsilon \in (0,1)$    is the excess-distortion probability, and    $Q^{-1}$    is the inverse    $Q$   -function. We give a reverse waterfilling integral representation for the dispersion    $V(d)$   , which parallels that of the rate-distortion functions for Gaussian processes. Remarkably, for all    $0   ,    $R(n,d,\epsilon)$    of the Gauss–Markov source coincides with that of    $Z_{i}$   , the i.i.d. Gaussian noise driving the process, up to the second-order term. Among novel technical tools developed in this paper is a sharp approximation of the eigenvalues of the covariance matrix of    $n$    samples of the Gauss–Markov source, and a construction of a typical set using the maximum likelihood estimate of the parameter    $a$    based on    $n$    observations.

Includes members:
  Member "second order region for gray wyner network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "strong converse using change of measure arguments")
  Member "on the dispersions of three network information theory problems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gaussian multiple and random access in the finite blocklength regime")
  Member "lossy joint source channel coding in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finite sample analysis of image registration")
  Member "fixed length lossy compression in the finite blocklength regime":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "finite sample analysis of image registration")
  Member "the dispersion of lossy source coding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sparse regression codes")
  Member "the dispersion of joint source channel coding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a lower bound on the expected distortion of joint source channel coding")
  Member "a note on rate distortion functions for nonstationary gaussian autoregressive processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from parameter estimation to dispersion of nonstationary gauss markov processes")

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)