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: "incremental tradeoff resolution in qualitative probabilistic networks"
  Abstract: Qualitative probabilistic reasoning in a Bayesian network often reveals tradeoffs: relationships that are ambiguous due to competing qualitative influences. We present two techniques that combine qualitative and numeric probabilistic reasoning to resolve such tradeoffs, inferring the qualitative relationship between nodes in a Bayesian network. The first approach incrementally marginalizes nodes that contribute to the ambiguous qualitative relationships. The second approach evaluates approximate Bayesian networks for bounds of probability distributions, and uses these bounds to determinate qualitative relationships in question. This approach is also incremental in that the algorithm refines the state spaces of random variables for tighter bounds until the qualitative relationships are resolved. Both approaches provide systematic methods for tradeoff resolution at potentially lower computational cost than application of purely numeric methods.

Connected through hyperedge HE-1 (induced by source paper "using qualitative relationships for bounding probability distributions"):
  with members:
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "robustness analysis of bayesian networks with local convex sets of distributions"
    - "path planning under time dependent uncertainty"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "localized partial evaluation of belief networks"
    - "propagation of 2 monotone lower probabilities on an undirected graph"
    - "incremental probabilistic inference"

Connected through hyperedge HE-2 (induced by source paper "pivotal pruning of trade offs in qpns"):
  with members:
    - "intercausal reasoning with uninstantiated ancestor nodes"
    - "enhancing qpns for trade off resolution"
    - "refining reasoning in qualitative probabilistic networks"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-3 (induced by source paper "enhancing qpns for trade off resolution"):
  with members:
    - "refining reasoning in qualitative probabilistic networks"

Connected through hyperedge HE-4 (induced by source paper "the structure of signals causal interdependence models for games of incomplete information"):
  with members:
    - "networks of influence diagrams a formalism for representing agents beliefs and decision making processes"

Connected through hyperedge HE-5 (induced by source paper "monotonicity in bayesian networks"):
  with members:
    - "map complexity results and approximation methods"

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)