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: "strategy selection in influence diagrams using imprecise probabilities"
  Abstract: This paper describes a new algorithm to solve the decision making problem in Influence Diagrams based on algorithms for credal networks. Decision nodes are associated to imprecise probability distributions and a reformulation is introduced that finds the global maximum strategy with respect to the expected utility. We work with Limited Memory Influence Diagrams, which generalize most Influence Diagram proposals and handle simultaneous decisions. Besides the global optimum method, we explore an anytime approximate solution with a guaranteed maximum error and show that imprecise probabilities are handled in a straightforward way. Complexity issues and experiments with random diagrams and an effects-based military planning problem are discussed.

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"
    - "from influence diagrams to junction trees"
    - "a differential approach to inference in bayesian networks"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"
    - "sensitivity analysis in decision circuits"
    - "three new sensitivity analysis methods for influence diagrams"
    - "when do numbers really matter"

Connected through hyperedge HE-2 (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"
    - "from influence diagrams to junction trees"
    - "an anytime algorithm for decision making under uncertainty"
    - "efficient value of information computation"
    - "solving multistage influence diagrams using branch and bound search"
    - "solving limited memory influence diagrams"

Connected through hyperedge HE-3 (induced by source paper "the complexity of approximately solving influence diagrams"):
  with members:
    - "solving pomdps by searching the space of finite policies"
    - "a method for using belief networks as influence diagrams"
    - "solving limited memory influence diagrams"
    - "complexity results and approximation strategies for map explanations"

Connected through hyperedge HE-4 (induced by source paper "multi objective influence diagrams"):
  with members:
    - "from influence diagrams to junction trees"
    - "a method for using belief networks as influence diagrams"

Connected through hyperedge HE-5 (induced by source paper "solving limited memory influence diagrams"):
  with members:
    - "decision making using probabilistic inference methods"
    - "a method for using belief networks as influence diagrams"
    - "new results for the map problem in bayesian networks"
    - "an algebraic graphical model for decision with uncertainties feasibilities and utilities"
    - "complexity results and approximation strategies for map explanations"

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)