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: "advances in probabilistic reasoning"
  Abstract: This paper discuses multiple Bayesian networks representation paradigms for encoding asymmetric independence assertions. We offer three contributions: (1) an inference mechanism that makes explicit use of asymmetric independence to speed up computations, (2) a simplified definition of similarity networks and extensions of their theory, and (3) a generalized representation scheme that encodes more types of asymmetric independence assertions than do similarity networks.

Connected through hyperedge HE-1 (induced by source paper "multi context models for reasoning under partial knowledge generative process and inference grammar"):
  with members:
    - "context specific independence in bayesian networks"
    - "chain graphs for learning"
    - "towards precision of probabilistic bounds propagation"
    - "complexity of inference in graphical models"

Connected through hyperedge HE-2 (induced by source paper "representing and combining partially specified cpts"):
  with members:
    - "context specific approximation in probabilistic inference"
    - "elicitation of probabilities for belief networks combining qualitative and quantitative information"
    - "constructing situation specific belief networks"
    - "learning bayesian networks with local structure"
    - "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-3 (induced by source paper "network fragments representing knowledge for constructing probabilistic models"):
  with members:
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "causal independence for knowledge acquisition and inference"
    - "model based influence diagrams for machine vision"
    - "context specific independence in bayesian networks"
    - "knowledge engineering for large belief networks"
    - "object oriented bayesian networks"
    - "utility based control for computer vision"

Connected through hyperedge HE-4 (induced by source paper "context specific independence in bayesian networks"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "causal independence for knowledge acquisition and inference"
    - "on heuristics for finding loop cutsets in multiply connected belief networks"
    - "learning bayesian networks with local structure"
    - "a probabilistic calculus of actions"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"

Connected through hyperedge HE-5 (induced by source paper "structured arc reversal and simulation of dynamic probabilistic networks"):
  with members:
    - "sensor validation using dynamic belief networks"
    - "a computational scheme for reasoning in dynamic probabilistic networks"
    - "learning bayesian networks with local structure"
    - "a graph theoretic analysis of information value"
    - "context specific independence in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "network engineering for complex belief networks"):
  with members:
    - "exploring localization in bayesian networks for large expert systems"
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "inference algorithms for similarity networks"
    - "abstraction in belief networks the role of intermediate states in diagnostic reasoning"
    - "knowledge engineering for large belief networks"

Connected through hyperedge HE-7 (induced by source paper "incremental probabilistic inference"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "local expression languages for probabilistic dependence a preliminary report"

Connected through hyperedge HE-8 (induced by source paper "sensitivity analysis for probability assessments in bayesian networks"):
  with members:
    - "a bayesian method for constructing bayesian belief networks from databases"
    - "theory refinement on bayesian 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)