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: "elicitation of probabilities for belief networks combining qualitative and quantitative information"
  Abstract: Although the usefulness of belief networks for reasoning under uncertainty is widely accepted, obtaining numerical probabilities that they require is still perceived a major obstacle. Often not enough statistical data is available to allow for reliable probability estimation. Available information may not be directly amenable for encoding in the network. Finally, domain experts may be reluctant to provide numerical probabilities. In this paper, we propose a method for elicitation of probabilities from a domain expert that is non-invasive and accommodates whatever probabilistic information the expert is willing to state. We express all available information, whether qualitative or quantitative in nature, in a canonical form consisting of (in) equalities expressing constraints on the hyperspace of possible joint probability distributions. We then use this canonical form to derive second-order probability distributions over the desired probabilities.

Connected through hyperedge HE-1 (induced by source paper "representing and combining partially specified cpts"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "constructing situation specific belief networks"
    - "learning bayesian networks with local structure"
    - "advances in probabilistic reasoning"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "a generalization of the noisy or model"
    - "context specific independence in bayesian networks"
    - "computing upper and lower bounds on likelihoods in intractable networks"

Connected through hyperedge HE-2 (induced by source paper "exploiting qualitative knowledge in the learning of conditional probabilities of bayesian networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "learning bayesian nets that perform well"
    - "accelerating em an empirical study"
    - "update rules for parameter estimation in bayesian networks"

Connected through hyperedge HE-3 (induced by source paper "user interface tools for navigation in conditional probability tables and elicitation of probabilities in bayesian networks"):
  with members:
    - "context specific independence in bayesian networks"
    - "conditional independence in possibility theory"

Connected through hyperedge HE-4 (induced by source paper "problem focused incremental elicitation of multi attribute utility models"):
  with members:
    - "graphical models for preference and utility"

Connected through hyperedge HE-5 (induced by source paper "belief updating and learning in semi qualitative probabilistic networks"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "exploiting qualitative knowledge in the learning of conditional probabilities of bayesian networks"
    - "on the relation between kappa calculus and probabilistic reasoning"
    - "upgrading ambiguous signs in qpns"
    - "monotonicity in bayesian networks"
    - "complexity results and approximation strategies for map explanations"

Connected through hyperedge HE-6 (induced by source paper "upgrading ambiguous signs in qpns"):
  with members:
    - "from qualitative to quantitative probabilistic networks"

Connected through hyperedge HE-7 (induced by source paper "ipf for discrete chain factor graphs"):
  with members:
    - "chain graphs for learning"

Connected through hyperedge HE-8 (induced by source paper "logical inference algorithms and matrix representations for probabilistic conditional independence"):
  with members:
    - "on the conditional independence implication problem a lattice theoretic approach"

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)