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 "on a class of markov order estimators based on ppm and other universal codes"):
  Abstract: We investigate a class of estimators of the Markov order for stationary ergodic processes which form a slight modification of the construction by Merhav, Gutman, and Ziv (1989). Both kinds of estimators compare the estimate of the entropy rate given by a universal code with the empirical conditional entropy of a string and return the order for which the two quantities are approximately equal. However, our modification, which we call universal Markov orders, satisfies a few attractive properties, not shown by Merhav, Gutman, and Ziv (1989) for their original definition. Firstly, the universal Markov orders are almost surely consistent, without any restrictions. Secondly, they are upper bounded asymptotically by the logarithm of the string length divided by the entropy rate. Thirdly, if we choose the Prediction by Partial Matching (PPM) as the universal code then the number of distinct substrings of the length equal to the universal Markov order constitutes an upper bound for the block mutual information. Thus universal Markov orders can be also used indirectly for quantification of long memory for an ergodic process.

Includes members:
  Member "scaling laws for neural language models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "train large then compress rethinking model size for efficient training and inference of transformers")
  Member "calibration entropy rates and memory in language models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sticking to the facts confident decoding for faithful data to text generation")
  Member "regular hilberg processes an example of processes with a vanishing entropy rate":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "consistency of the plug in estimator of the entropy rate for ergodic processes")
  Member "mixing ergodic and nonergodic processes with rapidly growing information between blocks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "consistency of the plug in estimator of the entropy rate for ergodic processes")
  Member "estimating the entropy of binary time series methodology some theory and a simulation study":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "practical prediction of human movements across device types and spatiotemporal granularities")

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)