human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "formal verification of higher order probabilistic programs reasoning about approximation convergence bayesian inference and optimization"):
  Abstract: Probabilistic programming provides a convenient lingua franca for writing succinct and rigorous descriptions of probabilistic models and inference tasks. Several probabilistic programming languages, including Anglican, Church or Hakaru, derive their expressiveness from a powerful combination of continuous distributions, conditioning, and higher-order functions. Although very important for practical applications, these features raise fundamental challenges for program semantics and verification. Several recent works offer promising answers to these challenges, but their primary focus is on foundational semantics issues. #R##N##R##N#In this paper, we take a step further by developing a suite of logics, collectively named PPV for proving properties of programs written in an expressive probabilistic higher-order language with continuous sampling operations and primitives for conditioning distributions. Our logics mimic the comfortable reasoning style of informal proofs using carefully selected axiomatizations of key results from probability theory. The versatility of our logics is illustrated through the formal verification of several intricate examples from statistics, probabilistic inference, and machine learning. We further show expressiveness by giving sound embeddings of existing logics. In particular, we do this in a parametric way by showing how the semantics idea of (unary and relational) ⊤⊤-lifting can be internalized in our logics. The soundness of PPV follows by interpreting programs and assertions in quasi-Borel spaces (QBS), a recently proposed variant of Borel spaces with a good structure for interpreting higher order probabilistic programs.

Includes members:
  Member "equivalence between wasserstein and value aware model based reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "policy aware model learning for policy gradient methods")
  Member "relational reasoning for markov chains in a probabilistic guarded lambda calculus":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a separation logic for concurrent randomized programs")
  Member "measurable cones and stable measurable functions a model for probabilistic higher order programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the linear structure of cones")
  Member "mixed powerdomains for probability and nondeterminism":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the ideal approach to computing closed subsets in well quasi orderings")
  Member "a convenient category for higher order probability theory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards verified stochastic variational inference for probabilistic programs")
  Member "denotational validation of higher order bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "correctness of sequential monte carlo inference for probabilistic programming languages")
  Member "a theory of slicing for probabilistic control flow graphs":
    also connected through other hyperedges:
      (none)
  Member "a relational logic for higher order programs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "operationally based program equivalence proofs using lctrss")

Question: Please tell me which class the source paper 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)