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: "knowledge engineering for large belief networks"
  Abstract: We present several techniques for knowledge engineering of large belief networks (BNs) based on the our experiences with a network derived from a large medical knowledge base. The noisyMAX, a generalization of the noisy-OR gate, is used to model causal in dependence in a BN with multi-valued variables. We describe the use of leak probabilities to enforce the closed-world assumption in our model. We present Netview, a visualization tool based on causal independence and the use of leak probabilities. The Netview software allows knowledge engineers to dynamically view sub-networks for knowledge engineering, and it provides version control for editing a BN. Netview generates sub-networks in which leak probabilities are dynamically updated to reflect the missing portions of the network.

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"
    - "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"

Connected through hyperedge HE-2 (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"
    - "advances in probabilistic reasoning"
    - "context specific independence in bayesian networks"
    - "object oriented bayesian networks"
    - "utility based control for computer vision"

Connected through hyperedge HE-3 (induced by source paper "optimal monte carlo estimation of belief network inference"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "improved sampling for diagnostic reasoning in bayesian networks"
    - "backward simulation in bayesian networks"
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "simulation approaches to general probabilistic inference on belief networks"

Connected through hyperedge HE-4 (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"
    - "advances in probabilistic reasoning"
    - "inference algorithms for similarity networks"
    - "abstraction in belief networks the role of intermediate states in diagnostic reasoning"

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"
    - "the use of conflicts in searching bayesian networks"
    - "a new look at causal independence"

Connected through hyperedge HE-6 (induced by source paper "computational complexity reduction for bn2o networks using similarity of states"):
  with members:
    - "a tractable inference algorithm for diagnosing multiple diseases"
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "a new look at causal independence"
    - "state space abstraction for anytime evaluation of probabilistic networks"

Connected through hyperedge HE-7 (induced by source paper "inference with causal independence in the cpsc network"):
  with members:
    - "causal independence for knowledge acquisition and inference"
    - "a new look at causal independence"
    - "a generalization of the noisy or model"
    - "inter causal independence and heterogeneous factorization"

Connected through hyperedge HE-8 (induced by source paper "confidence inference in bayesian networks"):
  with members:
    - "weighing and integrating evidence for stochastic simulation in bayesian networks"
    - "simulation approaches to general probabilistic inference on belief networks"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large 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)