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: "pruning bayesian networks for efficient computation"
  Abstract: This paper analyzes the circumstances under which Bayesian networks can be pruned in order to reduce computational complexity without altering the computation for variables of interest. Given a problem instance which consists of a query and evidence for a set of nodes in the network, it is possible to delete portions of the network which do not participate in the computation for the query. Savings in computational complexity can be large when the original network is not singly connected. Results analogous to those described in this paper have been derived before [Geiger, Verma, and Pearl 89, Shachter 88] but the implications for reducing complexity of the computations in Bayesian networks have not been stated explicitly. We show how a preprocessing step can be used to prune a Bayesian network prior to using standard algorithms to solve a given problem instance. We also show how our results can be used in a parallel distributed implementation in order to achieve greater savings. We define a computationally equivalent subgraph of a Bayesian network. The algorithm developed in [Geiger, Verma, and Pearl 89] is modified to construct the subgraphs described in this paper with O(e) complexity, where e is the number of edges in the Bayesian network. Finally, we define a minimal computationally equivalent subgraph and prove that the subgraphs described are minimal.

Connected through hyperedge HE-1 (induced by source paper "lazy factored inference for functional probabilistic programming"):
  with members:
    - "generating bayesian networks from probability logic knowledge bases"
    - "expectation propagation for approximate bayesian inference"
    - "dynamic construction of belief networks"
    - "church a language for generative models"
    - "an empirical evaluation of possible variations of lazy propagation"

Connected through hyperedge HE-2 (induced by source paper "constructing situation specific belief networks"):
  with members:
    - "a theoretical framework for context sensitive temporal probability model construction with application to plan projection"
    - "generating bayesian networks from probability logic knowledge bases"
    - "network fragments representing knowledge for constructing probabilistic models"
    - "d separation from theorems to algorithms"
    - "integrating model construction and evaluation"
    - "tradeoffs in constructing and evaluating temporal influence diagrams"
    - "computational advantages of relevance reasoning in bayesian belief networks"

Connected through hyperedge HE-3 (induced by source paper "the use of conflicts in searching bayesian networks"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "a new algorithm for finding map assignments to belief networks"

Connected through hyperedge HE-4 (induced by source paper "exploiting causal independence in bayesian network inference"):
  with members:
    - "causal independence for knowledge acquisition and inference"
    - "a new look at causal independence"
    - "parameter adjustment in bayes networks the generalized noisy or gate"
    - "additive belief network models"
    - "context specific independence in bayesian networks"
    - "d separation from theorems to algorithms"
    - "knowledge engineering for large belief 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)