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 constraint propagation approach to probabilistic reasoning"
  Abstract: The paper demonstrates that strict adherence to probability theory does not preclude the use of concurrent, self-activated constraint-propagation mechanisms for managing uncertainty. Maintaining local records of sources-of-belief allows both predictive and diagnostic inferences to be activated simultaneously and propagate harmoniously towards a stable equilibrium.

Connected through hyperedge HE-1 (induced by source paper "local conditioning exact message passing for cyclic undirected distributed networks"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-2 (induced by source paper "joint causal inference from multiple contexts"):
  with members:
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "ancestral causal inference"
    - "distinguishing cause from effect using observational data methods and benchmarks"
    - "strong completeness and faithfulness in bayesian networks"
    - "learning sparse causal models is not np hard"
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "causal discovery from changes"
    - "a general algorithm for deciding transportability of experimental results"

Connected through hyperedge HE-3 (induced by source paper "discrete bayesian networks the exact posterior marginal distributions"):
  with members:
    - "a comparison of lauritzen spiegelhalter hugin and shenoy shafer architectures for computing marginals of probability distributions"
    - "incremental dynamic construction of layered polytree networks"
    - "evidence absorption and propagation through evidence reversals"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-4 (induced by source paper "global conditioning for probabilistic inference in belief networks"):
  with members:
    - "approximations in bayesian belief universe for knowledge based systems"
    - "a combination of cutset conditioning with clique tree propagation in the pathfinder system"
    - "evidence absorption and propagation through evidence reversals"
    - "local expression languages for probabilistic dependence a preliminary report"

Connected through hyperedge HE-5 (induced by source paper "a graph based inference method for conditional independence"):
  with members:
    - "on the logic of causal models"
    - "causal networks semantics and expressiveness"
    - "d separation from theorems to algorithms"

Connected through hyperedge HE-6 (induced by source paper "a method for using belief networks as influence diagrams"):
  with members:
    - "updating probabilities in multiply connected belief networks"
    - "intelligent probabilistic inference"
    - "stochastic simulation of bayesian belief networks"

Connected through hyperedge HE-7 (induced by source paper "causal networks semantics and expressiveness"):
  with members:
    - "on the logic of causal models"
    - "intelligent probabilistic inference"

Connected through hyperedge HE-8 (induced by source paper "simulation approaches to general probabilistic inference on belief networks"):
  with members:
    - "temporal reasoning with probabilities"
    - "stochastic simulation of bayesian 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)