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: "disintegration and bayesian inversion both abstractly and concretely"
  Abstract: The notions of disintegration and Bayesian inversion are fundamental in conditional probability theory. They produce channels, as conditional probabilities, from a joint state, or from an already given channel (in opposite direction). These notions exist in the literature, in concrete situations, but are presented here in abstract graphical formulations. The resulting abstract descriptions are used for proving basic results in conditional probability theory. The existence of disintegration and Bayesian inversion is discussed for discrete probability, and also for measure-theoretic probability --- via standard Borel spaces and via likelihoods. Finally, the usefulness of disintegration and Bayesian inversion is illustrated in several non-trivial examples.

Connected through hyperedge HE-1 (induced by source paper "lower and upper conditioning in quantum bayesian theory"):
  with members:
    - "the logical essentials of bayesian reasoning"
    - "an introduction to effectus theory"

Connected through hyperedge HE-2 (induced by source paper "the mathematics of changing one s mind via jeffrey s or via pearl s update rule"):
  with members:
    - "lower and upper conditioning in quantum bayesian theory"
    - "a channel based exact inference algorithm for bayesian networks"
    - "the logical essentials of bayesian reasoning"
    - "a recipe for state and effect triangles"
    - "hyper normalisation and conditioning for discrete probability distributions"

Connected through hyperedge HE-3 (induced by source paper "causal inference by string diagram surgery"):
  with members:
    - "the logical essentials of bayesian reasoning"
    - "pearl s calculus of intervention is complete"

Connected through hyperedge HE-4 (induced by source paper "distances between states and between predicates"):
  with members:
    - "abstract hidden markov models a monadic account of quantitative information flow"
    - "mixed powerdomains for probability and nondeterminism"
    - "a recipe for state and effect triangles"
    - "an introduction to effectus theory"
    - "behavioral metrics via functor lifting"

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)