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: "approximations in bayesian belief universe for knowledge based systems"
  Abstract: When expert systems based on causal probabilistic networks (CPNs) reach a certain size and complexity, the "combinatorial explosion monster" tends to be present. We propose an approximation scheme that identifies rarely occurring cases and excludes these from being processed as ordinary cases in a CPN-based expert system. Depending on the topology and the probability distributions of the CPN, the numbers (representing probabilities of state combinations) in the underlying numerical representation can become very small. Annihilating these numbers and utilizing the resulting sparseness through data structuring techniques often results in several orders of magnitude of improvement in the consumption of computer resources. Bounds on the errors introduced into a CPN-based expert system through approximations are established. Finally, reports on empirical studies of applying the approximation scheme to a real-world CPN are given.

Connected through hyperedge HE-1 (induced by source paper "a tutorial on learning with bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "directed reduction algorithms and decomposable graphs"
    - "a transformational characterization of equivalent bayesian network structures"
    - "computing upper and lower bounds on likelihoods in intractable networks"
    - "theory refinement on bayesian networks"
    - "a bayesian approach to learning causal networks"

Connected through hyperedge HE-2 (induced by source paper "an advance on variable elimination with applications to tensor based computation"):
  with members:
    - "efficient contraction of large tensor networks for weighted model counting through graph decompositions"
    - "on the relative expressiveness of bayesian and neural networks"
    - "on relaxing determinism in arithmetic circuits"
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
    - "a differential approach to inference in bayesian networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-3 (induced by source paper "survey on models and techniques for root cause analysis"):
  with members:
    - "learning possibilistic logic theories from default rules"
    - "learning tractable probabilistic models for fault localization"
    - "using abduction in markov logic networks for root cause analysis"
    - "automated reasoning using possibilistic logic semantics belief revision and variable certainty weights"
    - "loopy belief propagation for approximate inference an empirical study"
    - "a differential approach to inference in bayesian networks"
    - "evaluating computational models of explanation using human judgments"
    - "exact inference of hidden structure from sample data in noisy or networks"

Connected through hyperedge HE-4 (induced by source paper "weighted positive binary decision diagrams for exact probabilistic inference"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "context specific independence in bayesian networks"
    - "using robdds for inference in bayesian networks with troubleshooting as an example"
    - "sum product networks a new deep architecture"
    - "counting belief propagation"
    - "and or multi valued decision diagrams aomdds for graphical models"

Connected through hyperedge HE-5 (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"
    - "causal independence for knowledge acquisition and inference"
    - "the use of conflicts in searching bayesian networks"
    - "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-6 (induced by source paper "a differential approach to inference in bayesian networks"):
  with members:
    - "query dags a practical paradigm for implementing belief network inference"
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "approximating map using local search"
    - "making sensitivity analysis computationally efficient"
    - "map complexity results and approximation methods"
    - "when do numbers really matter"
    - "a knowledge compilation map"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-7 (induced by source paper "a variational approximation for bayesian networks with discrete and continuous latent variables"):
  with members:
    - "nonuniform dynamic discretization in hybrid networks"
    - "inference using message propagation and topology transformation in vector gaussian continuous networks"
    - "optimal junction trees"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "global conditioning for probabilistic inference in belief networks"
    - "a sufficiently fast algorithm for finding close to optimal junction trees"
    - "variational probabilistic inference and the qmr dt network"

Connected through hyperedge HE-8 (induced by source paper "context specific independence in bayesian networks"):
  with members:
    - "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"
    - "advances in probabilistic reasoning"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"

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)