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 "streaming algorithms for optimal generation of random bits"):
  Abstract: Generating random bits from a source of biased coins (the biased is unknown) is a classical question that was originally studied by von Neumann. There are a number of known algorithms that have asymptotically optimal information efficiency, namely, the expected number of generated random bits per input bit is asymptotically close to the entropy of the source. However, only the original von Neumann algorithm has a 'streaming property' - it operates on a single input bit at a time and it generates random bits when possible, alas, it does not have an optimal information efficiency. #R##N#The main contribution of this paper is an algorithm that generates random bit streams from biased coins, uses bounded space and runs in expected linear time. As the size of the allotted space increases, the algorithm approaches the information-theoretic upper bound on efficiency. In addition, we discuss how to extend this algorithm to generate random bit streams from m-sided dice or correlated sources such as Markov chains.

Includes members:
  Member "a universal scheme for transforming binary algorithms to generate random bits from loaded dice":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "optimal algorithms for universal random number generation from finite memory sources")
  Member "efficient generation of random bits from finite state markov chains":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "optimal algorithms for universal random number generation from finite memory sources")

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)