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: "domains and random variables"
  Abstract: The aim of this paper is to establish a theory of random variables on domains. Domain theory is a fundamental component of theoretical computer science, providing mathematical models of computational processes. Random variables are the mainstay of probability theory. Since computational models increasingly involve probabilistic aspects, it's only natural to explore the relationship between these two areas. Our main results show how to cast results about random variables using a domain-theoretic approach. The pay-off is an extension of the results from probability measures to sub-probability measures. We also use our approach to extend the class of domains for which we can classify the domain structure of the space of sub-probability measures.

Connected through hyperedge HE-1 (induced by source paper "a domain theory for statistical probabilistic programming"):
  with members:
    - "an application of computable distributions to the semantics of probabilistic programs"
    - "factorisation systems for logical relations and monadic lifting in type and effect system semantics"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "a convenient category for higher order probability theory"
    - "denotational validation of higher order bayesian inference"
    - "semantics for probabilistic programming higher order functions continuous distributions and soft constraints"
    - "reasoning about recursive probabilistic programs"
    - "a lambda calculus foundation for universal probabilistic programming"

Connected through hyperedge HE-2 (induced by source paper "measurable cones and stable measurable functions a model for probabilistic higher order programming"):
  with members:
    - "an application of computable distributions to the semantics of probabilistic programs"
    - "mixed powerdomains for probability and nondeterminism"
    - "a convenient category for higher order probability theory"
    - "convexity and order in probabilistic call by name fpc"
    - "probabilistic call by push value"
    - "a lambda calculus foundation for universal probabilistic programming"

Connected through hyperedge HE-3 (induced by source paper "probabilistic call by push value"):
  with members:
    - "mixed powerdomains for probability and nondeterminism"
    - "a convenient category for higher order probability theory"
    - "convexity and order in probabilistic call by name fpc"
    - "full abstraction for probabilistic pcf"

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)