human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "directed reduction algorithms and decomposable graphs"
  Abstract: In recent years, there have been intense research efforts to develop efficient methods for probabilistic inference in probabilistic influence diagrams or belief networks. Many people have concluded that the best methods are those based on undirected graph structures, and that those methods are inherently superior to those based on node reduction operations on the influence diagram. We show here that these two approaches are essentially the same, since they are explicitly or implicity building and operating on the same underlying graphical structures. In this paper we examine those graphical structures and show how this insight can lead to an improved class of directed reduction methods.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "a transformational characterization of equivalent bayesian network structures"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "theory refinement on bayesian networks"
    - "a bayesian approach to learning causal networks"

Connected through hyperedge HE-2 (induced by source paper "logarithmic time parallel bayesian inference"):
  with members:
    - "logarithmic time updates and queries in probabilistic networks"
    - "evidence absorption and propagation through evidence reversals"
    - "parallelizing probabilistic inference some early explorations"
    - "global conditioning for probabilistic inference in belief networks"

Connected through hyperedge HE-3 (induced by source paper "using potential influence diagrams for probabilistic inference and decision making"):
  with members:
    - "decision making using probabilistic inference methods"
    - "a fusion algorithm for solving bayesian decision problems"
    - "d separation from theorems to algorithms"
    - "evidence absorption and propagation through evidence reversals"

Connected through hyperedge HE-4 (induced by source paper "graphical representations of consensus belief"):
  with members:
    - "the topological fusion of bayes nets"
    - "some complexity considerations in the combination of belief networks"

Connected through hyperedge HE-5 (induced by source paper "compact securities markets for pareto optimal reallocation of risk"):
  with members:
    - "graphical representations of consensus belief"

Connected through hyperedge HE-6 (induced by source paper "dynamic programming in in uence diagrams with decision circuits"):
  with members:
    - "decision making using probabilistic inference methods"
    - "bayes ball the rational pastime for determining irrelevance and requisite information in belief networks and influence diagrams"
    - "sensitivity analysis in decision circuits"

Question: Please tell me which class the center node 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.AI(Artificial Intelligence)