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: "evidence absorption and propagation through evidence reversals"
  Abstract: The arc reversal/node reduction approach to probabilistic inference is extended to include the case of instantiated evidence by an operation called "evidence reversal." This not only provides a technique for computing posterior joint distributions on general belief networks, but also provides insight into the methods of Pearl [1986b] and Lauritzen and Spiegelhalter [1988]. Although it is well understood that the latter two algorithms are closely related, in fact all three algorithms are identical whenever the belief network is a forest.

Connected through hyperedge HE-1 (induced by source paper "too fast causal inference under causal insufficiency"):
  with members:
    - "minimum error tree decomposition"

Connected through hyperedge HE-2 (induced by source paper "fast restricted causal inference"):
  with members:
    - "minimum error tree decomposition"

Connected through hyperedge HE-3 (induced by source paper "learning belief network structure from data under causal insufficiency"):
  with members:
    - "minimum error tree decomposition"

Connected through hyperedge HE-4 (induced by source paper "real time top k predictive query processing over event streams"):
  with members:
    - "search based methods to bound diagnostic probabilities in very large belief nets"
    - "a bayesian approach to learning causal networks"
    - "ais bn an adaptive importance sampling algorithm for evidential reasoning in large bayesian networks"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-5 (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"
    - "a constraint propagation approach to probabilistic reasoning"
    - "exploiting causal independence in bayesian network inference"

Connected through hyperedge HE-6 (induced by source paper "a bayesian method for causal modeling and discovery under selection"):
  with members:
    - "learning bayesian networks the combination of knowledge and statistical data"
    - "asymptotic model selection for directed networks with hidden variables"
    - "causal discovery from a mixture of experimental and observational data"
    - "learning gaussian networks"
    - "causal inference in the presence of latent variables and selection bias"

Connected through hyperedge HE-7 (induced by source paper "logarithmic time parallel bayesian inference"):
  with members:
    - "logarithmic time updates and queries in probabilistic networks"
    - "directed reduction algorithms and decomposable graphs"
    - "parallelizing probabilistic inference some early explorations"
    - "global conditioning for probabilistic inference in belief networks"

Connected through hyperedge HE-8 (induced by source paper "using potential influence diagrams for probabilistic inference and decision making"):
  with members:
    - "decision making using probabilistic inference methods"
    - "a fusion algorithm for solving bayesian decision problems"
    - "directed reduction algorithms and decomposable graphs"
    - "d separation from theorems to algorithms"

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)