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 "bayes optimal sequential multi hypothesis testing in exponential families"):
  Abstract: Bayesian sequential testing of multiple simple hypotheses is a classical sequential decision problem. However, the optimal policy is computationally intractable in general, as the posterior probability space is exponentially increasing in the number of hypotheses (the curse of dimensionality in state space). We consider a specialized problem in which observations are drawn from the same exponential family. By reconstructing the posterior probability vector from a low-dimensional diagnostic sufficient statistic, it is shown that the intrinsic dimension of the reachable posterior probability space is determined by the rank of a diagnostic matrix, which cannot exceed the number of parameters governing the exponential family, or the number of hypotheses, whichever is smaller. For univariate exponential families commonly used in practice, the probability space is of one or two dimension in most cases. Hence, the optimal policy can computed in an efficient manner. Geometric interpretation and illustrative examples are presented. Simulation studies suggest that the optimal policy can substantially outperform the existing method. The results are also extended to the sequential sampling control problem.

Includes members:
  Member "active sequential hypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "decentralized sequential active hypothesis testing and the mac feedback capacity")
  Member "controlled sensing for multihypothesis testing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "searching for anomalies over composite hypotheses")

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)