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: "kantorovich continuity of probabilistic programs"
  Abstract: The Kantorovich metric is a canonical lifting of a distance from sets to distributions over this set. The metric also arises naturally when proving continuity properties of probabilistic programs. For instance, algorithmic stability of machine learning algorithms is upper bounded by the maximal Kantorovich distance between program executions, for a suitable notion of metric on the underlying space. Motivated by these applications, we develop a sound method to approximate the Kantorovich distance between two executions of a probabilistic program. Our method takes the form of a relational pre-expectation calculus. We illustrate our methods for proving stability of machine learning algorithms and convergence of probabilistic processes.

Connected through hyperedge HE-1 (induced by source paper "proving expected sensitivity of probabilistic programs with randomized variable dependent termination time"):
  with members:
    - "new approaches for almost sure termination of probabilistic programs"
    - "computational approaches for stochastic shortest path on succinct mdps"
    - "bounded expectations resource analysis for probabilistic programs"
    - "proving expected sensitivity of probabilistic programs"
    - "finding polynomial loop invariants for probabilistic programs"
    - "a new proof rule for almost sure termination"
    - "a semantic account of metric preservation"
    - "lexicographic ranking supermartingales an efficient approach to termination of probabilistic programs"

Connected through hyperedge HE-2 (induced by source paper "proving expected sensitivity of probabilistic programs with randomized execution time"):
  with members:
    - "computational approaches for stochastic shortest path on succinct mdps"
    - "bounded expectations resource analysis for probabilistic programs"
    - "proving expected sensitivity of probabilistic programs"
    - "a semantic account of metric preservation"
    - "coupling proofs are probabilistic product programs"
    - "termination analysis of probabilistic programs through positivstellensatz s"
    - "weakest precondition reasoning for expected run times of probabilistic programs"
    - "train faster generalize better stability of stochastic gradient descent"

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)