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 making with interval influence diagrams"
  Abstract: In previous work (Fertig and Breese, 1989; Fertig and Breese, 1990) we defined a mechanism for performing probabilistic reasoning in influence diagrams using interval rather than point-valued probabilities. In this paper we extend these procedures to incorporate decision nodes and interval-valued value functions in the diagram. We derive the procedures for chance node removal (calculating expected value) and decision node removal (optimization) in influence diagrams where lower bounds on probabilities are stored at each chance node and interval bounds are stored on the value function associated with the diagram's value node. The output of the algorithm are a set of admissible alternatives for each decision variable and a set of bounds on expected value based on the imprecision in the input. The procedure can be viewed as an approximation to a full e-dimensional sensitivity analysis where n are the number of imprecise probability distributions in the input. We show the transformations are optimal and sound. The performance of the algorithm on an influence diagrams is investigated and compared to an exact algorithm.

Connected through hyperedge HE-1 (induced by source paper "credal networks under maximum entropy"):
  with members:
    - "coherent knowledge processing at maximum entropy by spirit"
    - "generating bayesian networks from probability logic knowledge bases"
    - "irrelevance and independence relations in quasi bayesian networks"
    - "from statistical knowledge bases to degrees of belief"
    - "probabilistic default reasoning with conditional constraints"
    - "probabilistic deduction with conditional constraints over basic events"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-2 (induced by source paper "robustness analysis of bayesian networks with local convex sets of distributions"):
  with members:
    - "independence with lower and upper probabilities"
    - "theoretical foundations for abstraction based probabilistic planning"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-3 (induced by source paper "irrelevance and independence relations in quasi bayesian networks"):
  with members:
    - "independence with lower and upper probabilities"
    - "independence concepts for convex sets of probabilities"
    - "robustness analysis of bayesian networks with local convex sets of distributions"

Connected through hyperedge HE-4 (induced by source paper "updating sets of probabilities"):
  with members:
    - "a new approach to updating beliefs"

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)