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: "regular hilberg processes an example of processes with a vanishing entropy rate"
  Abstract: A regular Hilberg process is a stationary process that satisfies both a power-law growth of topological entropy and a hyperlogarithmic growth of maximal repetition. Hypothetically, such processes may arise in statistical modeling of natural language. A striking property of ergodic regular Hilberg processes is that the length of the Lempel-Ziv code is orders of magnitude larger than the block entropy. This is possible since regular Hilberg processes have a vanishing entropy rate. In this paper, we provide a constructive example of regular Hilberg processes, which we call random hierarchical association (RHA) processes. We demonstrate that for those RHA processes, the expected length of any uniquely decodable code is orders of magnitude larger than the block entropy of the ergodic component of the RHA process. Our proposition complements the classical result by Shields concerning nonexistence of universal redundancy rates.

Connected through hyperedge HE-1 (induced by source paper "on a class of markov order estimators based on ppm and other universal codes"):
  with members:
    - "scaling laws for neural language models"
    - "calibration entropy rates and memory in language models"
    - "mixing ergodic and nonergodic processes with rapidly growing information between blocks"
    - "estimating the entropy of binary time series methodology some theory and a simulation study"

Connected through hyperedge HE-2 (induced by source paper "consistency of the plug in estimator of the entropy rate for ergodic processes"):
  with members:
    - "minimax estimation of functionals of discrete distributions"
    - "mixing ergodic and nonergodic processes with rapidly growing information between blocks"
    - "applications of universal source coding to statistical analysis of time series"

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)