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: "cost analysis of nondeterministic probabilistic programs"
  Abstract: We consider the problem of expected cost analysis over nondeterministic probabilistic programs, which aims at automated methods for analyzing the resource-usage of such programs. Previous approaches for this problem could only handle nonnegative bounded costs. However, in many scenarios, such as queuing networks or analysis of cryptocurrency protocols, both positive and negative costs are necessary and the costs are unbounded as well. #R##N#In this work, we present a sound and efficient approach to obtain polynomial bounds on the expected accumulated cost of nondeterministic probabilistic programs. Our approach can handle (a) general positive and negative costs with bounded updates in variables; and (b) nonnegative costs with general updates to variables. We show that several natural examples which could not be handled by previous approaches are captured in our framework. #R##N#Moreover, our approach leads to an efficient polynomial-time algorithm, while no previous approach for cost analysis of probabilistic programs could guarantee polynomial runtime. Finally, we show the effectiveness of our approach by presenting experimental results on a variety of programs, motivated by real-world applications, for which we efficiently synthesize tight resource-usage bounds.

Connected through hyperedge HE-1 (induced by source paper "tail bound analysis for probabilistic programs via central moments"):
  with members:
    - "aiming low is harder inductive proof rules for lower bounds on weakest preexpectations in probabilistic program verification"
    - "bounded expectations resource analysis for probabilistic programs"
    - "termination of nondeterministic recursive probabilistic programs"
    - "termination analysis of probabilistic programs through positivstellensatz s"
    - "weakest precondition reasoning for expected run times of probabilistic programs"
    - "reasoning about recursive probabilistic programs"
    - "a lambda calculus foundation for universal probabilistic programming"

Connected through hyperedge HE-2 (induced by source paper "modular verification for almost sure termination of probabilistic programs"):
  with members:
    - "quantitative separation logic a logic for reasoning about probabilistic pointer programs"
    - "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"
    - "termination of nondeterministic recursive probabilistic programs"
    - "a new proof rule for almost sure termination"
    - "lexicographic ranking supermartingales an efficient approach to termination of probabilistic programs"
    - "cantor meets scott semantic foundations for probabilistic networks"

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.PL(Programming Languages)