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: "synthesizing coupling proofs of differential privacy"
  Abstract: Differential privacy has emerged as a promising probabilistic formulation of privacy, generating intense interest within academia and industry. We present a push-button, automated technique for verifying e-differential privacy of sophisticated randomized algorithms. We make several conceptual, algorithmic, and practical contributions: (i) Inspired by the recent advances on approximate couplings and randomness alignment, we present a new proof technique called coupling strategies, which casts differential privacy proofs as a winning strategy in a game where we have finite privacy resources to expend. (ii) To discover a winning strategy, we present a constraint-based formulation of the problem as a set of Horn modulo couplings (HMC) constraints, a novel combination of first-order Horn clauses and probabilistic constraints. (iii) We present a technique for solving HMC constraints by transforming probabilistic constraints into logical constraints with uninterpreted functions. (iv) Finally, we implement our technique in the FairSquare verifier and provide the first automated privacy proofs for a number of challenging algorithms from the differential privacy literature, including Report Noisy Max, the Exponential Mechanism, and the Sparse Vector Mechanism.

Connected through hyperedge HE-1 (induced by source paper "guidelines for implementing and auditing differentially private systems"):
  with members:
    - "differentially private objective perturbation beyond smoothness and convexity"
    - "identifying cache based side channels through secret augmented abstract interpretation"
    - "fuzzi a three level logic for differential privacy"
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "private selection from private candidates"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "how to use heuristics for differential privacy"

Connected through hyperedge HE-2 (induced by source paper "free gap information from the differentially private sparse vector and noisy max mechanisms"):
  with members:
    - "proving differential privacy with shadow execution"
    - "private selection from private candidates"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-3 (induced by source paper "a programming framework for differential privacy with accuracy concentration bounds"):
  with members:
    - "fuzzi a three level logic for differential privacy"
    - "proving differential privacy with shadow execution"
    - "ektelo a framework for defining differentially private computations"
    - "apex accuracy aware differentially private data exploration"
    - "renyi differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"
    - "concentrated differential privacy simplifications extensions and lower bounds"

Connected through hyperedge HE-4 (induced by source paper "proving differential privacy with shadow execution"):
  with members:
    - "property testing for differential privacy"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "relational symbolic execution"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"

Connected through hyperedge HE-5 (induced by source paper "fuzzi a three level logic for differential privacy"):
  with members:
    - "approximate span liftings compositional semantics for relaxations of differential privacy"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "learning differentially private recurrent language models"
    - "towards practical differential privacy for sql queries"
    - "lightdp towards automating differential privacy proofs"
    - "probabilistic couplings for probabilistic reasoning"
    - "psi ψ a private data sharing interface"

Connected through hyperedge HE-6 (induced by source paper "the complexity of verifying loop free programs as differentially private"):
  with members:
    - "automated methods for checking differential privacy"
    - "property testing for differential privacy"
    - "bisimilarity distances for approximate differential privacy"
    - "detecting violations of differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "understanding the sparse vector technique for differential privacy"
    - "proving differential privacy via probabilistic couplings"
    - "the complexity of computing the optimal composition of differential privacy"

Connected through hyperedge HE-7 (induced by source paper "approximate span liftings compositional semantics for relaxations of differential privacy"):
  with members:
    - "renyi differential privacy"
    - "liftings for 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-8 (induced by source paper "automated methods for checking differential privacy"):
  with members:
    - "bisimilarity distances for approximate differential privacy"
    - "detecting violations of differential privacy"
    - "advanced probabilistic couplings for differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "a program logic for union bounds"
    - "proving differential privacy via probabilistic couplings"
    - "calibrating data to sensitivity in private data analysis"
    - "iterative constructions and private data release"

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)