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: "stationary and transition probabilities in slow mixing long memory markov processes"
  Abstract: We observe a length-$n$ sample generated by an unknown,stationary ergodic Markov process (\emph{model}) over a finite alphabet $\mathcal{A}$. Given any string $\bf{w}$ of symbols from $\mathcal{A}$ we want estimates of the conditional probability distribution of symbols following $\bf{w}$, as well as the stationary probability of $\bf{w}$. Two distinct problems that complicate estimation in this setting are (i) long memory, and (ii) \emph{slow mixing} which could happen even with only one bit of memory. #R##N#Any consistent estimator in this setting can only converge pointwise over the class of all ergodic Markov models. Namely, given any estimator and any sample size $n$, the underlying model could be such that the estimator performs poorly on a sample of size $n$ with high probability. But can we look at a length-$n$ sample and identify \emph{if} an estimate is likely to be accurate? #R##N#Since the memory is unknown \emph{a-priori}, a natural approach is to estimate a potentially coarser model with memory $k_n=\mathcal{O}(\log n)$. As $n$ grows, pointwise consistent estimates that hold eventually almost surely (eas) are known so long as the scaling of $k_n$ is not superlogarithmic in $n$. Here, rather than eas convergence results, we want the best answers possible with a length-$n$ sample. Combining results in universal compression with Aldous' coupling arguments, we obtain sufficient conditions on the length-$n$ sample (even for slow mixing models) to identify when naive (i) estimates of the conditional probabilities and (ii) estimates related to the stationary probabilities are accurate; and also bound the deviations of the naive estimates from true values.

Connected through hyperedge HE-1 (induced by source paper "community detection using slow mixing markov models"):
  with members:
    - "exact recovery in the stochastic block model"
    - "community detection thresholds and the weak ramanujan property"
    - "a tutorial on spectral clustering"

Connected through hyperedge HE-2 (induced by source paper "on the performance of short block codes over finite state channels in the rare transition regime"):
  with members:
    - "delay sensitive communication over fading channel queueing behavior and code parameter selection"
    - "entropy rate for hidden markov chains with rare transitions"

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)