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: "decision theoretic troubleshooting a framework for repair and experiment"
  Abstract: We develop and extend existing decision-theoretic methods for troubleshooting a nonfunctioning device. Traditionally, diagnosis with Bayesian networks has focused on belief updating---determining the probabilities of various faults given current observations. In this paper, we extend this paradigm to include taking actions. In particular, we consider three classes of actions: (1) we can make observations regarding the behavior of a device and infer likely faults as in traditional diagnosis, (2) we can repair a component and then observe the behavior of the device to infer likely faults, and (3) we can change the configuration of the device, observe its new behavior, and infer the likelihood of faults. Analysis of latter two classes of troubleshooting actions requires incorporating notions of persistence into the belief-network formalism used for probabilistic inference.

Connected through hyperedge HE-1 (induced by source paper "towards accountable ai hybrid human machine analyses for characterizing system failure"):
  with members:
    - "manipulating and measuring model interpretability"
    - "interpretable explorable approximations of black box models"
    - "an analysis of iso 26262 using machine learning safely in automotive software"
    - "on human intellect and machine failures troubleshooting integrative machine learning systems"
    - "spice semantic propositional image caption evaluation"
    - "concrete problems in ai safety"
    - "why should i trust you explaining the predictions of any classifier"
    - "cider consensus based image description evaluation"

Connected through hyperedge HE-2 (induced by source paper "constrained bayesian networks theory optimization and applications"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "sensitivity analysis for probability assessments in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "on human intellect and machine failures troubleshooting integrative machine learning systems"):
  with members:
    - "debugging machine learning tasks"
    - "oracle performance for visual captioning"
    - "cider consensus based image description evaluation"
    - "from captions to visual concepts and back"
    - "microsoft coco common objects in context"

Connected through hyperedge HE-4 (induced by source paper "probabilities of causation bounds and identification"):
  with members:
    - "axiomatizing causal reasoning"
    - "testing identifiability of causal effects"
    - "a probabilistic calculus of actions"
    - "psychological and normative theories of causal power and the probabilities of causes"
    - "counterfactuals and policy analysis in structural models"

Connected through hyperedge HE-5 (induced by source paper "decision theoretic troubleshooting hardness of approximation"):
  with members:
    - "a polynomial algorithm for computing the optimal repair strategy in a system with independent component failures"
    - "the cost of troubleshooting cost clusters with inside information"

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)