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: "the logical essentials of bayesian reasoning"
  Abstract: This chapter offers an accessible introduction to the channel-based approach to Bayesian probability theory. This framework rests on algebraic and logical foundations, inspired by the methodologies of programming language semantics. It offers a uniform, structured and expressive language for describing Bayesian phenomena in terms of familiar programming concepts, like channel, predicate transformation and state transformation. The introduction also covers inference in Bayesian networks, which will be modelled by a suitable calculus of string diagrams.

Connected through hyperedge HE-1 (induced by source paper "lower and upper conditioning in quantum bayesian theory"):
  with members:
    - "disintegration and bayesian inversion both abstractly and concretely"
    - "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"
    - "disintegration and bayesian inversion both abstractly and concretely"
    - "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:
    - "disintegration and bayesian inversion both abstractly and concretely"
    - "pearl s calculus of intervention is complete"

Connected through hyperedge HE-4 (induced by source paper "disintegration and bayesian inversion via string diagrams"):
  with members:
    - "measure transformer semantics for bayesian machine learning"

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)