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: "evaluating influence diagrams with decision circuits"
  Abstract: Although a number of related algorithms have been developed to evaluate influence diagrams, exploiting the conditional independence in the diagram, the exact solution has remained intractable for many important problems. In this paper we introduce decision circuits as a means to exploit the local structure usually found in decision problems and to improve the performance of influence diagram analysis. This work builds on the probabilistic inference algorithms using arithmetic circuits to represent Bayesian belief networks [Darwiche, 2003]. Once compiled, these arithmetic circuits efficiently evaluate probabilistic queries on the belief network, and methods have been developed to exploit both the global and local structure of the network. We show that decision circuits can be constructed in a similar fashion and promise similar benefits.

Connected through hyperedge HE-1 (induced by source paper "deep tractable probabilistic models for moral responsibility"):
  with members:
    - "learning and testing causal models with interventions"
    - "towards formal definitions of blameworthiness intention and moral responsibility"
    - "a computational model of commonsense moral decision making"
    - "a voting based system for ethical decision making"
    - "on the latent variable interpretation in sum product networks"
    - "sum product networks a new deep architecture"
    - "solving sat and bayesian inference with backtracking search"

Connected through hyperedge HE-2 (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"
    - "sensitivity analysis in bayesian networks from single to multiple parameters"
    - "on the robustness of most probable explanations"
    - "when do numbers really matter"

Connected through hyperedge HE-3 (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"
    - "sensitivity analysis in decision circuits"

Connected through hyperedge HE-4 (induced by source paper "solving multistage influence diagrams using branch and bound search"):
  with members:
    - "probabilistic inference in influence diagrams"
    - "from influence diagrams to junction trees"
    - "an algorithm for finding minimum d separating sets in belief networks"
    - "an anytime algorithm for decision making under uncertainty"
    - "unconstrained influence diagrams"

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)