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: "the use of conflicts in searching bayesian networks"
  Abstract: This paper discusses how conflicts (as used by the consistency-based diagnosis community) can be adapted to be used in a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popular Bayesian net algorithms exploit the structure of the network for efficiency, we exploit probability distributions for efficiency; this algorithm is most suited to the case with extreme probabilities. This paper presents a solution to the inefficiencies found in naive algorithms, and shows how the tools of the consistency-based diagnosis community (namely conflicts) can be used effectively to improve the efficiency. Empirical results with networks having tens of thousands of nodes are presented.

Connected through hyperedge HE-1 (induced by source paper "independence of causal influence and clique tree propagation"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "utility based abstraction and categorization"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "causal independence for knowledge acquisition and inference"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "abstraction in belief networks the role of intermediate states in diagnostic reasoning"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-2 (induced by source paper "sample and accumulate algorithms for belief updating in bayes networks"):
  with members:
    - "some properties of joint probability distributions"
    - "a stratified simulation scheme for inference in bayesian belief networks"
    - "backward simulation in bayesian networks"
    - "a new look at causal independence"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "incremental probabilistic inference"

Connected through hyperedge HE-3 (induced by source paper "some experiments with real time decision algorithms"):
  with members:
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "fast belief update using order of magnitude probabilities"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "state space abstraction for anytime evaluation of probabilistic networks"
    - "localized partial evaluation of belief networks"
    - "incremental probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "fast belief update using order of magnitude probabilities"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "parallel belief revision"
    - "bounded conditioning flexible inference for decisions under scarce resources"
    - "local expression languages for probabilistic dependence a preliminary report"

Connected through hyperedge HE-5 (induced by source paper "efficient search based inference for noisy or belief networks topepsilon"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "some properties of joint probability distributions"
    - "a new look at causal independence"
    - "knowledge engineering for large belief networks"

Connected through hyperedge HE-6 (induced by source paper "the probability of a possibility adding uncertainty to default rules"):
  with members:
    - "modal logics for qualitative possibility and beliefs"
    - "reasoning with qualitative probabilities can be tractable"
    - "nonmonotonic reasoning preferential models and cumulative logics"

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)