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: "sensitivity analysis in decision circuits"
  Abstract: Decision circuits have been developed to perform efficient evaluation of influence diagrams [Bhattacharjya and Shachter, 2007], building on the advances in arithmetic circuits for belief network inference [Darwiche,2003]. In the process of model building and analysis, we perform sensitivity analysis to understand how the optimal solution changes in response to changes in the model. When sequential decision problems under uncertainty are represented as decision circuits, we can exploit the efficient solution process embodied in the decision circuit and the wealth of derivative information available to compute the value of information for the uncertainties in the problem and the effects of changes to model parameters on the value and the optimal strategy.

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"
    - "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 "three new sensitivity analysis methods for influence diagrams"):
  with members:
    - "a measure of decision flexibility"
    - "a differential approach to inference in bayesian networks"
    - "making sensitivity analysis computationally efficient"
    - "analysing sensitivity data from probabilistic networks"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"
    - "evaluating influence diagrams with decision circuits"

Connected through hyperedge HE-3 (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"
    - "directed reduction algorithms and decomposable graphs"

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)