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: "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.

Connected through hyperedge HE-1 (induced by source paper "the next 700 relational program logics"):
  with members:
    - "synthetic topology in homotopy type theory for probabilistic programming"
    - "journey beyond full abstraction exploring robust property preservation for secure compilation"
    - "proof relevant logical relations for name generation"
    - "relational cost analysis for functional imperative programs"
    - "coupling proofs are probabilistic product programs"
    - "a relational logic for higher order programs"
    - "relational reasoning via probabilistic coupling"
    - "abstract effects and proof relevant logical relations"

Connected through hyperedge HE-2 (induced by source paper "the geometry of bayesian programming"):
  with members:
    - "a domain theory for statistical probabilistic programming"
    - "measurable cones and stable measurable functions a model for probabilistic higher order programming"
    - "a convenient category for higher order probability theory"
    - "the geometry of parallelism classical probabilistic and quantum effects"
    - "a lambda calculus foundation for universal probabilistic programming"
    - "full abstraction for probabilistic pcf"
    - "church a language for generative models"

Connected through hyperedge HE-3 (induced by source paper "a relational logic for higher order programs"):
  with members:
    - "coupling proofs are probabilistic product programs"
    - "deductive verification with relational properties"
    - "higher order approximate relational refinement types for mechanism design and differential privacy"
    - "logical step indexed logical relations"
    - "classical predicative logic enriched type theories"

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)