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 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.

Connected through hyperedge HE-1 (induced by source paper "from parameter estimation to dispersion of nonstationary gauss markov processes"):
  with members:
    - "non asymptotic identification of lti systems from a single trajectory"
    - "near optimal finite time identification of arbitrary linear dynamical systems"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "finite time identification in unstable linear systems"
    - "fixed length lossy compression in the finite blocklength regime"
    - "the dispersion of lossy source coding"
    - "a note on rate distortion functions for nonstationary gaussian autoregressive processes"

Connected through hyperedge HE-2 (induced by source paper "sampling of the wiener process for remote estimation over a channel with random delay"):
  with members:
    - "remote estimation over a packet drop channel with markovian state"
    - "scheduling status updates to minimize age of information with an energy harvesting sensor"
    - "age optimal information updates in multihop networks"
    - "remote estimation of the wiener process over a channel with random delay"
    - "the distortion rate function of sampled wiener processes"
    - "update or wait how to keep your data fresh"
    - "optimizing data freshness throughput and delay in multi server information update systems"
    - "the age of information real time status updating by multiple sources"

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)