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: "reasoning with qualitative probabilities can be tractable"
  Abstract: We recently described a formalism for reasoning with if-then rules that re expressed with different levels of firmness [18]. The formalism interprets these rules as extreme conditional probability statements, specifying orders of magnitude of disbelief, which impose constraints over possible rankings of worlds. It was shown that, once we compute a priority function Z+ on the rules, the degree to which a given query is confirmed or denied can be computed in O(log n`) propositional satisfiability tests, where n is the number of rules in the knowledge base. In this paper, we show that computing Z+ requires O(n2 X log n) satisfiability tests, not an exponential number as was conjectured in [18], which reduces to polynomial complexity in the case of Horn expressions. We also show how reasoning with imprecise observations can be incorporated in our formalism and how the popular notions of belief revision and epistemic entrenchment are embodied naturally and tractably.

Connected through hyperedge HE-1 (induced by source paper "incremental tradeoff resolution in qualitative probabilistic networks"):
  with members:
    - "using qualitative relationships for bounding probability distributions"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "refining reasoning in qualitative probabilistic networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-2 (induced by source paper "action networks a framework for reasoning about actions and change under uncertainty"):
  with members:
    - "causality in bayesian belief networks"
    - "a decision based view of causality"
    - "argument calculus and networks"
    - "from conditional oughts to qualitative decision theory"

Connected through hyperedge HE-3 (induced by source paper "the probability of a possibility adding uncertainty to default rules"):
  with members:
    - "modal logics for qualitative possibility and beliefs"
    - "the use of conflicts in searching bayesian networks"
    - "nonmonotonic reasoning preferential models and cumulative logics"

Connected through hyperedge HE-4 (induced by source paper "on the relation between kappa calculus and probabilistic reasoning"):
  with members:
    - "parallel belief revision"
    - "non monotonic reasoning and the reversibility of belief change"
    - "a general non probabilistic theory of inductive reasoning"

Connected through hyperedge HE-5 (induced by source paper "general belief measures"):
  with members:
    - "a general non probabilistic theory of inductive reasoning"
    - "the probability of a possibility adding uncertainty to default rules"

Connected through hyperedge HE-6 (induced by source paper "entailment in probability of thresholded generalizations"):
  with members:
    - "from statistical knowledge bases to degrees of belief"
    - "random worlds and maximum entropy"

Connected through hyperedge HE-7 (induced by source paper "why is diagnosis using belief networks insensitive to imprecision in probabilities"):
  with members:
    - "knowledge engineering for large belief networks"

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)