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 "three new sensitivity analysis methods for influence diagrams"):
  Abstract: Performing sensitivity analysis for influence diagrams using the decision circuit framework is particularly convenient, since the partial derivatives with respect to every parameter are readily available [Bhattacharjya and Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity analysis methods that utilize this partial derivative information and therefore do not require re-evaluating the decision situation multiple times. Specifically, we show how to efficiently compare strategies in decision situations, perform sensitivity to risk aversion and compute the value of perfect hedging [Seyller, 2008].

Includes members:
  Member "a measure of decision flexibility":
    also connected through other hyperedges:
      (none)
  Member "a differential approach to inference in bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interventions and counterfactuals in tractable probabilistic models limitations of contemporary transformations")
  Member "making sensitivity analysis computationally efficient":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the relative expressiveness of bayesian and neural networks")
  Member "analysing sensitivity data from probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sensitivity analysis in decision circuits")
  Member "sensitivity analysis in bayesian networks from single to multiple parameters":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a symbolic algebra for the computation of expected utilities in multiplicative influence diagrams")
  Member "sensitivity analysis in decision circuits":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a symbolic algebra for the computation of expected utilities in multiplicative influence diagrams")
  Member "evaluating influence diagrams with decision circuits":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep tractable probabilistic models for moral responsibility")

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.AI(Artificial Intelligence)