human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "using qualitative relationships for bounding probability distributions"):
  Abstract: We exploit qualitative probabilistic relationships among variables for computing bounds of conditional probability distributions of interest in Bayesian networks. Using the signs of qualitative relationships, we can implement abstraction operations that are guaranteed to bound the distributions of interest in the desired direction. By evaluating incrementally improved approximate networks, our algorithm obtains monotonically tightening bounds that converge to exact distributions. For supermodular utility functions, the tightening bounds monotonically reduce the set of admissible decision alternatives as well.

Includes members:
  Member "bounded conditioning flexible inference for decisions under scarce resources":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "state space abstraction for anytime evaluation of probabilistic networks")
  Member "incremental tradeoff resolution in qualitative probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pivotal pruning of trade offs in qpns")
  Member "robustness analysis of bayesian networks with local convex sets of distributions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "separation properties of sets of probability measures")
  Member "path planning under time dependent uncertainty":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust belief roadmap planning under intermittent sensing")
  Member "computing upper and lower bounds on likelihoods in intractable networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a tutorial on learning with bayesian networks")
  Member "state space abstraction for anytime evaluation of probabilistic networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "independence of causal influence and clique tree propagation")
  Member "localized partial evaluation of belief networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a graph theoretic analysis of information value")
  Member "propagation of 2 monotone lower probabilities on an undirected graph":
    also connected through other hyperedges:
      (none)

Question: Please tell me which class the source paper 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)