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 probability estimation via relative frequencies and discount"):
  Abstract: Probability estimation is an elementary building block of every statistical data compression algorithm. In practice probability estimation is often based on relative letter frequencies which get scaled down, when their sum is too large. Such algorithms are attractive in terms of memory requirements, running time and practical performance. However, there still is a lack of theoretical understanding. In this work we formulate a typical probability estimation algorithm based on relative frequencies and frequency discount, Algorithm RFD. Our main contribution is its theoretical analysis. We show that the code length it requires above an arbitrary piecewise stationary model with bounded and unbounded letter probabilities is small. This theoretically confirms the recency effect of periodic frequency discount, which has often been observed empirically.

Includes members:
  Member "partition tree weighting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generalized probability smoothing")
  Member "context tree switching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low complexity nonparametric bayesian online prediction with universal guarantees")
  Member "combining non stationary prediction optimization and mixing for data compression":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generalized probability smoothing")

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)