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: "liftings for differential privacy"
  Abstract: Recent developments in formal verification have identified approximate liftings (also known as approximate couplings) as a clean, compositional abstraction for proving differential privacy. There are two styles of definitions for this construction. Earlier definitions require the existence of one or more witness distributions, while a recent definition by Sato uses universal quantification over all sets of samples. These notions have different strengths and weaknesses: the universal version is more general than the existential ones, but the existential versions enjoy more precise composition principles. #R##N#We propose a novel, existential version of approximate lifting, called *-lifting, and show that it is equivalent to Sato's construction for discrete probability measures. Our work unifies all known notions of approximate lifting, giving cleaner properties, more general constructions, and more precise composition theorems for both styles of lifting, enabling richer proofs of differential privacy. We also clarify the relation between existing definitions of approximate lifting, and generalize our constructions to approximate liftings based on f-divergences.

Connected through hyperedge HE-1 (induced by source paper "approximate span liftings compositional semantics for relaxations of differential privacy"):
  with members:
    - "synthesizing coupling proofs of differential privacy"
    - "renyi differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "approximate relational hoare logic for continuous random samplings"
    - "differentially private bayesian programming"
    - "higher order approximate relational refinement types for mechanism design and differential privacy"

Connected through hyperedge HE-2 (induced by source paper "proving expected sensitivity of probabilistic programs"):
  with members:
    - "a semantic account of metric preservation"
    - "developing bug free machine learning systems with formal mathematics"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "coupling proofs are probabilistic product programs"
    - "probabilistic couplings for probabilistic reasoning"
    - "computer aided verification for mechanism design"
    - "advanced probabilistic couplings for differential privacy"
    - "without replacement sampling for stochastic gradient methods convergence results and application to distributed optimization"

Connected through hyperedge HE-3 (induced by source paper "synthesizing coupling proofs of differential privacy"):
  with members:
    - "proving uniformity and independence by self composition and coupling"
    - "a semantic account of metric preservation"
    - "coupling proofs are probabilistic product programs"
    - "lightdp towards automating differential privacy proofs"
    - "probabilistic couplings for probabilistic reasoning"
    - "advanced probabilistic couplings for differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "approximate relational hoare logic for continuous random samplings"

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)