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: "constraint propagation with imprecise conditional probabilities"
  Abstract: An approach to reasoning with default rules where the proportion of exceptions, or more generally the probability of encountering an exception, can be at least roughly assessed is presented. It is based on local uncertainty propagation rules which provide the best bracketing of a conditional probability of interest from the knowledge of the bracketing of some other conditional probabilities. A procedure that uses two such propagation rules repeatedly is proposed in order to estimate any simple conditional probability of interest from the available knowledge. The iterative procedure, that does not require independence assumptions, looks promising with respect to the linear programming method. Improved bounds for conditional probabilities are given when independence assumptions hold.

Connected through hyperedge HE-1 (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"
    - "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 "magic inference rules for probabilistic deduction under taxonomic knowledge"):
  with members:
    - "probabilistic description logics"
    - "a symbolic approach to reasoning with linguistic quantifiers"
    - "probabilistic deduction with conditional constraints over basic events"

Connected through hyperedge HE-3 (induced by source paper "independence concepts for convex sets of probabilities"):
  with members:
    - "combination of upper and lower probabilities"

Connected through hyperedge HE-4 (induced by source paper "empirical probabilities in monadic deductive databases"):
  with members:
    - "non monotonic negation in probabilistic deductive databases"

Connected through hyperedge HE-5 (induced by source paper "nonmonotonic probabilistic logics between model theoretic probabilistic logic and probabilistic logic under coherence"):
  with members:
    - "probabilistic description logics"
    - "belief functions and default reasoning"
    - "probabilistic logic programming under inheritance with overriding"
    - "from statistical knowledge bases to degrees of belief"
    - "what does a conditional knowledge base entail"
    - "another perspective on default reasoning"
    - "nonmonotonic reasoning preferential models and cumulative logics"
    - "probabilistic default reasoning with conditional constraints"

Connected through hyperedge HE-6 (induced by source paper "probabilistic default reasoning with conditional constraints"):
  with members:
    - "generating new beliefs from old"
    - "probabilistic description logics"
    - "belief functions and default reasoning"
    - "a modification to evidential probability"
    - "probabilistic logic programming under inheritance with overriding"
    - "computing reference classes"
    - "from statistical knowledge bases to degrees of belief"
    - "what does a conditional knowledge base entail"

Connected through hyperedge HE-7 (induced by source paper "probabilistic deduction with conditional constraints over basic events"):
  with members:
    - "anytime decision making with imprecise probabilities"
    - "from statistical knowledge bases to degrees of belief"

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)