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: "towards precision of probabilistic bounds propagation"
  Abstract: The DUCK-calculus presented here is a recent approach to cope with probabilistic uncertainty in a sound and efficient way. Uncertain rules with bounds for probabilities and explicit conditional independences can be maintained incrementally. The basic inference mechanism relies on local bounds propagation, implementable by deductive databases with a bottom-up fixpoint evaluation. In situations, where no precise bounds are deducible, it can be combined with simple operations research techniques on a local scope. In particular, we provide new precise analytical bounds for probabilistic entailment.

Connected through hyperedge HE-1 (induced by source paper "multi context models for reasoning under partial knowledge generative process and inference grammar"):
  with members:
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "chain graphs for learning"
    - "complexity of inference in graphical models"

Connected through hyperedge HE-2 (induced by source paper "propagation of 2 monotone lower probabilities on an undirected graph"):
  with members:
    - "independence with lower and upper probabilities"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "probabilistic description logics"):
  with members:
    - "a symbolic approach to reasoning with linguistic quantifiers"

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)