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: "from influence diagrams to junction trees"
  Abstract: We present an approach to the solution of decision problems formulated as influence diagrams. This approach involves a special triangulation of the underlying graph, the construction of a junction tree with special properties, and a message passing algorithm operating on the junction tree for computation of expected utilities and optimal decision policies.

Connected through hyperedge HE-1 (induced by source paper "a symbolic algebra for the computation of expected utilities in multiplicative influence diagrams"):
  with members:
    - "sensitivity analysis multilinearity and beyond"
    - "a differential approach to inference in bayesian networks"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"
    - "sensitivity analysis in decision circuits"
    - "strategy selection in influence diagrams using imprecise probabilities"
    - "three new sensitivity analysis methods for influence diagrams"
    - "when do numbers really matter"

Connected through hyperedge HE-2 (induced by source paper "directed expected utility networks"):
  with members:
    - "expected utility networks"
    - "local utility elicitation in gai models"

Connected through hyperedge HE-3 (induced by source paper "influence diagrams for the optimization of a vehicle speed profile"):
  with members:
    - "decision making using probabilistic inference methods"
    - "a method for using belief networks as influence diagrams"
    - "solving factored mdps with hybrid state and action variables"

Connected through hyperedge HE-4 (induced by source paper "flexible policy construction by information refinement"):
  with members:
    - "decision making using probabilistic inference methods"
    - "some properties of joint probability distributions"
    - "the compilation of decision models"
    - "an empirical evaluation of a randomized algorithm for probabilistic inference"
    - "deliberation scheduling for time critical sequential decision making"
    - "two procedures for compiling influence diagrams"
    - "exploiting causal independence in bayesian network inference"
    - "provably bounded optimal agents"

Connected through hyperedge HE-5 (induced by source paper "propagation of 2 monotone lower probabilities on an undirected graph"):
  with members:
    - "independence with lower and upper probabilities"
    - "constraint propagation with imprecise conditional probabilities"
    - "minimal assumption distribution propagation in belief networks"
    - "a new approach to updating beliefs"
    - "optimal junction trees"
    - "quasi bayesian strategies for efficient plan generation application to the planning to observe problem"
    - "an efficient implementation of belief function propagation"
    - "towards precision of probabilistic bounds propagation"

Connected through hyperedge HE-6 (induced by source paper "solving limited memory influence diagrams using branch and bound search"):
  with members:
    - "decision making using probabilistic inference methods"
    - "solving influence diagrams using hugin shafer shenoy and lazy propagation"
    - "an anytime algorithm for decision making under uncertainty"
    - "efficient value of information computation"
    - "solving multistage influence diagrams using branch and bound search"
    - "strategy selection in influence diagrams using imprecise probabilities"
    - "solving limited memory influence diagrams"

Connected through hyperedge HE-7 (induced by source paper "myopic value of information in influence diagrams"):
  with members:
    - "value of evidence on influence diagrams"
    - "decision making using probabilistic inference methods"
    - "a graph theoretic analysis of information value"
    - "using potential influence diagrams for probabilistic inference and decision making"

Connected through hyperedge HE-8 (induced by source paper "lazy evaluation of symmetric bayesian decision problems"):
  with members:
    - "decision making using probabilistic inference methods"
    - "d separation from theorems to algorithms"
    - "lazy propagation in junction trees"
    - "a method for using belief networks as influence diagrams"

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)