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: "detecting violations of differential privacy"
  Abstract: The widespread acceptance of differential privacy has led to the publication of many sophisticated algorithms for protecting privacy. However, due to the subtle nature of this privacy definition, many such algorithms have bugs that make them violate their claimed privacy. In this paper, we consider the problem of producing counterexamples for such incorrect algorithms. The counterexamples are designed to be short and human-understandable so that the counterexample generator can be used in the development process -- a developer could quickly explore variations of an algorithm and investigate where they break down. Our approach is statistical in nature. It runs a candidate algorithm many times and uses statistical tests to try to detect violations of differential privacy. An evaluation on a variety of incorrect published algorithms validates the usefulness of our approach: it correctly rejects incorrect algorithms and provides counterexamples for them within a few seconds.

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"
    - "how to use heuristics for differential privacy"
    - "synthesizing coupling proofs of differential privacy"

Connected through hyperedge HE-2 (induced by source paper "evaluating differentially private machine learning in practice"):
  with members:
    - "differentially private model publishing for deep learning"
    - "privacy preserving collaborative deep learning with irregular participants"
    - "cache telepathy leveraging shared resource attacks to learn dnn architectures"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "dp admm admm based distributed learning with differential privacy"
    - "how to backdoor federated learning"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "efficient deep learning on multi source private data"

Connected through hyperedge HE-3 (induced by source paper "reconciling utility and membership privacy via knowledge distillation"):
  with members:
    - "stolen memories leveraging model memorization for calibrated white box membership inference"
    - "disparate vulnerability on the unfairness of privacy attacks against machine learning"
    - "evaluating differentially private machine learning in practice"
    - "zero shot knowledge distillation in deep networks"
    - "exploiting unintended feature leakage in collaborative learning"
    - "private model compression via knowledge distillation"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "i know what you see power side channel attack on convolutional neural network accelerators"

Connected through hyperedge HE-4 (induced by source paper "minimax rates of estimating approximate differential privacy"):
  with members:
    - "proving differential privacy with shadow execution"
    - "generative adversarial privacy"
    - "property testing for differential privacy"
    - "pacgan the power of two samples in generative adversarial networks"
    - "estimating mutual information for discrete continuous mixtures"
    - "collecting telemetry data privately"
    - "which distribution distances are sublinearly testable"
    - "breaking the bandwidth barrier geometrical adaptive entropy estimation"

Connected through hyperedge HE-5 (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"
    - "synthesizing coupling proofs 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-6 (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"
    - "synthesizing coupling proofs 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-7 (induced by source paper "secure and utility aware data collection with condensed local differential privacy"):
  with members:
    - "blender enabling local search with a hybrid differential privacy model"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "practical locally private heavy hitters"
    - "marginal release under local differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "collecting telemetry data privately"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "local private efficient protocols for succinct histograms"

Connected through hyperedge HE-8 (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"
    - "synthesizing coupling proofs 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"

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.CR(Cryptography and Security)