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: "measure transformer semantics for bayesian machine learning"
  Abstract: The Bayesian approach to machine learning amounts to computing posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution ...

Connected through hyperedge HE-1 (induced by source paper "generative datalog with continuous distributions"):
  with members:
    - "infinite probabilistic databases"
    - "pyro deep universal probabilistic programming"
    - "discrete continuous mixtures in probabilistic programming generalized semantics and inference algorithms"
    - "probabilistic databases with an infinite open world assumption"
    - "markov logic in infinite domains"

Connected through hyperedge HE-2 (induced by source paper "formal verification of higher order probabilistic programs reasoning about approximation convergence bayesian inference and optimization"):
  with members:
    - "equivalence between wasserstein and value aware model based reinforcement learning"
    - "relational reasoning for markov chains in a probabilistic guarded lambda calculus"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "mixed powerdomains for probability and nondeterminism"
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "a theory of slicing for probabilistic control flow graphs"
    - "a relational logic for higher order programs"

Connected through hyperedge HE-3 (induced by source paper "probabilistic programming with densities in slicstan efficient flexible and deterministic"):
  with members:
    - "a convenient category for higher order probability theory"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "tensorflow a system for large scale machine learning"
    - "bayesdb a probabilistic programming system for querying the probable implications of data"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "running probabilistic programs backwards"
    - "venture a higher order probabilistic programming platform with programmable inference"
    - "church a language for generative models"

Connected through hyperedge HE-4 (induced by source paper "symbolic exact inference for discrete probabilistic programs"):
  with members:
    - "approximate knowledge compilation by online collapsed importance sampling"
    - "inference and learning in probabilistic logic programs using weighted boolean formulas"
    - "inference in probabilistic logic programs using weighted cnf s"
    - "a knowledge compilation map"

Connected through hyperedge HE-5 (induced by source paper "a linear exponential comonad in s finite transition kernels and probabilistic coherent spaces"):
  with members:
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "geometry of interaction for mall via hughes vanglabbeek proof nets"
    - "full abstraction for probabilistic pcf"

Connected through hyperedge HE-6 (induced by source paper "an application of computable distributions to the semantics of probabilistic programs"):
  with members:
    - "a domain theory for statistical probabilistic programming"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "a convenient category for higher order probability theory"
    - "church a language for generative models"
    - "probabilistic operational semantics for the lambda calculus"
    - "computability of probability measures and martin lof randomness over metric spaces"

Connected through hyperedge HE-7 (induced by source paper "what s the over under probabilistic bounds on information leakage"):
  with members:
    - "securing databases from probabilistic inference"
    - "church a language for generative models"
    - "the parma polyhedra library toward a complete set of numerical abstractions for the analysis and verification of hardware and software systems"

Connected through hyperedge HE-8 (induced by source paper "cantor meets scott semantic foundations for probabilistic networks"):
  with members:
    - "event driven network programming"
    - "a fast compiler for netkat"

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.LO(Logic in Computer Science)