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: "independence with lower and upper probabilities"
  Abstract: It is shown that the ability of the interval probability representation to capture epistemological independence is severely limited. Two events are epistemologically independent if knowledge of the first event does not alter belief (i.e., probability bounds) about the second. However, independence in this form can only exist in a 2-monotone probability function in degenerate cases i.e., if the prior bounds are either point probabilities or entirely vacuous. Additional limitations are characterized for other classes of lower probabilities as well. It is argued that these phenomena are simply a matter of interpretation. They appear to be limitations when one interprets probability bounds as a measure of epistemological indeterminacy (i.e., uncertainty arising from a lack of knowledge), but are exactly as one would expect when probability intervals are interpreted as representations of ontological indeterminacy (indeterminacy introduced by structural approximations). The ontological interpretation is introduced and discussed.

Connected through hyperedge HE-1 (induced by source paper "propagation of 2 monotone lower probabilities on an undirected graph"):
  with members:
    - "from influence diagrams to junction trees"
    - "constraint propagation with imprecise conditional probabilities"
    - "minimal assumption distribution propagation in belief networks"
    - "a new approach to updating beliefs"
    - "optimal junction trees"
    - "quasi bayesian strategies for efficient plan generation application to the planning to observe problem"
    - "an efficient implementation of belief function propagation"
    - "towards precision of probabilistic bounds propagation"

Connected through hyperedge HE-2 (induced by source paper "separation properties of sets of probability measures"):
  with members:
    - "independence concepts for convex sets of probabilities"
    - "robustness analysis of bayesian networks with local convex sets of distributions"
    - "irrelevance and independence relations in quasi bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "robustness analysis of bayesian networks with local convex sets of distributions"):
  with members:
    - "theoretical foundations for abstraction based probabilistic planning"
    - "decision making with interval influence diagrams"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (induced by source paper "irrelevance and independence relations in quasi bayesian networks"):
  with members:
    - "independence concepts for convex sets of probabilities"
    - "robustness analysis of bayesian networks with local convex sets of distributions"
    - "decision making with interval 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)