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 "a preadapted universal switch distribution for testing hilberg s conjecture"):
  Abstract: Hilberg's conjecture about natural language states that the mutual information between two adjacent long blocks of text grows like a power of the block length. The exponent in this statement can be upper bounded using the pointwise mutual information estimate computed for a carefully chosen code. The bound is the better, the lower the compression rate is but there is a requirement that the code be universal. So as to improve a received upper bound for Hilberg's exponent, in this paper, we introduce two novel universal codes, called the plain switch distribution and the preadapted switch distribution. Generally speaking, switch distributions are certain mixtures of adaptive Markov chains of varying orders with some additional communication to avoid so called catch-up phenomenon. The advantage of these distributions is that they both achieve a low compression rate and are guaranteed to be universal. Using the switch distributions we obtain that a sample of a text in English is non-Markovian with Hilberg's exponent being $\le 0.83$, which improves over the previous bound $\le 0.94$ obtained using the Lempel-Ziv code.

Includes members:
  Member "universal codes from switching strategies":
    also connected through other hyperedges:
      (none)
  Member "on hilberg s law and its links with guiraud s law":
    also connected through other hyperedges:
      (none)

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)