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: "tail probabilities for randomized program runtimes via martingales for higher moments"
  Abstract: Programs with randomization constructs is an active research topic, especially after the recent introduction of martingale-based analysis methods for their termination and runtimes. Unlike most of the existing works that focus on proving almost-sure termination or estimating the expected runtime, in this work we study the tail probabilities of runtimes-such as "the execution takes more than 100 steps with probability at most 1%." To this goal, we devise a theory of supermartingales that overapproximate higher moments of runtime. These higher moments, combined with a suitable concentration inequality, yield useful upper bounds of tail probabilities. Moreover, our vector-valued formulation enables automated template-based synthesis of those supermartingales. Our experiments suggest the method's practical use.

Connected through hyperedge HE-1 (induced by source paper "automatic generation of moment based invariants for prob solvable loops"):
  with members:
    - "polynomial invariant generation for non deterministic recursive programs"
    - "aligator jl a julia package for loop invariant generation"
    - "finding polynomial loop invariants for probabilistic programs"
    - "a new proof rule for almost sure termination"
    - "a storm is coming a modern probabilistic model checker"
    - "synthesizing probabilistic invariants via doob s decomposition"
    - "bounded model checking for probabilistic programs"
    - "counterexample guided polynomial loop invariant generation by lagrange interpolation"

Connected through hyperedge HE-2 (induced by source paper "polynomial probabilistic invariants and the optional stopping theorem"):
  with members:
    - "polynomial invariant generation for non deterministic recursive programs"
    - "compositional analysis for almost sure termination of probabilistic programs"
    - "aiming low is harder inductive proof rules for lower bounds on weakest preexpectations in probabilistic program verification"
    - "finding polynomial loop invariants for probabilistic programs"
    - "weakest precondition reasoning for expected run times of probabilistic programs"
    - "synthesizing probabilistic invariants via doob s decomposition"
    - "counterexample guided polynomial loop invariant generation by lagrange interpolation"

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)