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: "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
  Abstract: In the last decade, several architectures have been proposed for exact computation of marginals using local computation. In this paper, we compare three architectures - Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer - from the perspective of graphical structure for message propagation, message-passing scheme, computational efficiency, and storage efficiency.

Connected through hyperedge HE-1 (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"
    - "approximations in bayesian belief universe for knowledge based systems"
    - "a differential approach to inference in bayesian networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-2 (induced by source paper "scaling up hybrid probabilistic inference with logical and arithmetic constraints via message passing"):
  with members:
    - "efficient search based weighted model integration"
    - "probabilistic inference modulo theories"
    - "learning bayesian networks a unification for discrete and gaussian domains"

Connected through hyperedge HE-3 (induced by source paper "exact inference techniques for the analysis of bayesian attack graphs"):
  with members:
    - "a learning based approach to reactive security"

Connected through hyperedge HE-4 (induced by source paper "discrete bayesian networks the exact posterior marginal distributions"):
  with members:
    - "incremental dynamic construction of layered polytree networks"
    - "evidence absorption and propagation through evidence reversals"
    - "a constraint propagation approach to probabilistic reasoning"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-5 (induced by source paper "bayesian error bars for belief net inference"):
  with members:
    - "a tutorial on learning with bayesian networks"
    - "sensitivity analysis for probability assessments in bayesian networks"
    - "a differential approach to inference in bayesian networks"
    - "minimal assumption distribution propagation in belief networks"
    - "learning bayesian nets that perform well"
    - "an implementation of a method for computing the uncertainty in inferred probabilities in belief networks"
    - "context specific independence in bayesian networks"

Connected through hyperedge HE-6 (induced by source paper "solving influence diagrams using hugin shafer shenoy and lazy propagation"):
  with members:
    - "from influence diagrams to junction trees"
    - "nested junction trees"
    - "evaluating influence diagrams using limids"

Connected through hyperedge HE-7 (induced by source paper "new advances in inference by recursive conditioning"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "hybrid processing of beliefs and constraints"

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)