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 bayesian networks from single to multiple parameters"
  Abstract: Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes, and to identify single parameter changes that would enforce a certain query constraint. In this paper, we expand the work to multiple parameters which may be in the CPT of a single variable, or the CPTs of multiple variables. Not only do we identify the solution space of multiple parameter changes that would be needed to enforce a query constraint, but we also show how to find the optimal solution, that is, the one which disturbs the current probability distribution the least (with respect to a specific measure of disturbance). We characterize the computational complexity of our new techniques and discuss their applications to developing and debugging Bayesian networks, and to the problem of reasoning about the value (reliability) of new information.

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 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 "sensitivity analysis multilinearity and beyond"):
  with members:
    - "propagation using chain event graphs"
    - "when do numbers really matter"

Connected through hyperedge HE-3 (induced by source paper "sensitivity analysis in decision circuits"):
  with members:
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "making sensitivity analysis computationally efficient"
    - "analysing sensitivity data from probabilistic networks"
    - "evaluating influence diagrams with decision circuits"
    - "on the robustness of most probable explanations"
    - "when do numbers really matter"

Connected through hyperedge HE-4 (induced by source paper "evaluating influence 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"
    - "a definition and graphical representation for causality"
    - "from influence diagrams to junction trees"
    - "a differential approach to inference in bayesian networks"
    - "on the robustness of most probable explanations"

Connected through hyperedge HE-5 (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 decision circuits"
    - "evaluating influence diagrams with decision circuits"

Connected through hyperedge HE-6 (induced by source paper "exploiting evidence in probabilistic inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "a differential approach to inference in bayesian networks"
    - "context specific independence in bayesian networks"
    - "evidence absorption and propagation through evidence reversals"
    - "solving map exactly using systematic search"

Connected through hyperedge HE-7 (induced by source paper "on the robustness of most probable explanations"):
  with members:
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "making sensitivity analysis computationally efficient"
    - "analysing sensitivity data from probabilistic networks"
    - "when do numbers really matter"

Connected through hyperedge HE-8 (induced by source paper "sensitivity analysis for threshold decision making with dynamic networks"):
  with members:
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "making sensitivity analysis computationally efficient"
    - "analysing sensitivity data from probabilistic networks"
    - "when do numbers really matter"

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)