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: "plausible deniability for privacy preserving data synthesis"
  Abstract: Releasing full data records is one of the most challenging problems in data privacy. On the one hand, many of the popular techniques such as data de-identification are problematic because of their dependence on the background knowledge of adversaries. On the other hand, rigorous methods such as the exponential mechanism for differential privacy are often computationally impractical to use for releasing high dimensional data or cannot preserve high utility of original data due to their extensive data perturbation. #R##N#This paper presents a criterion called plausible deniability that provides a formal privacy guarantee, notably for releasing sensitive datasets: an output record can be released only if a certain amount of input records are indistinguishable, up to a privacy parameter. This notion does not depend on the background knowledge of an adversary. Also, it can efficiently be checked by privacy tests. We present mechanisms to generate synthetic datasets with similar statistical properties to the input data and the same format. We study this technique both theoretically and experimentally. A key theoretical result shows that, with proper randomization, the plausible deniability mechanism generates differentially private synthetic data. We demonstrate the efficiency of this generative technique on a large dataset; it is shown to preserve the utility of original data with respect to various statistical analysis and machine learning measures.

Connected through hyperedge HE-1 (induced by source paper "privgen preserving privacy of sequences through data generation"):
  with members:
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "privacy preserving prediction"
    - "understanding membership inferences on well generalized learning models"
    - "k τ e anonymity towards privacy preserving publishing of spatiotemporal trajectory data"
    - "generating multi label discrete patient records using generative adversarial networks"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "privacy risk in machine learning analyzing the connection to overfitting"
    - "privtree a differentially private algorithm for hierarchical decompositions"

Connected through hyperedge HE-2 (induced by source paper "leveraging hierarchical representations for preserving privacy and utility in text"):
  with members:
    - "poincare wasserstein autoencoder"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "privacy preserving active learning on sensitive data for user intent classification"
    - "generalised differential privacy for text document processing"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "poincar e glove hyperbolic word embeddings"
    - "technical privacy metrics a systematic survey"

Connected through hyperedge HE-3 (induced by source paper "privacy and utility preserving textual analysis via calibrated multivariate perturbations"):
  with members:
    - "auditing data provenance in text generation models"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "technical privacy metrics a systematic survey"
    - "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"
    - "enriching word vectors with subword information"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "membership inference attacks against machine learning models"
    - "applying deep learning to answer selection a study and an open task"

Connected through hyperedge HE-4 (induced by source paper "privacy preserving blockchain based federated learning with differential data sharing"):
  with members:
    - "distributed federated learning for ultra reliable low latency vehicular communications"
    - "a tutorial on computing t closeness"
    - "privacy preserving deep learning via weight transmission"
    - "a secure and robust scheme for sharing confidential information in iot systems"
    - "towards federated learning at scale system design"
    - "federated machine learning concept and applications"
    - "building scalable decentralized payment systems"
    - "chiron privacy preserving machine learning as a service"

Connected through hyperedge HE-5 (induced by source paper "drynx decentralized secure verifiable system for statistical queries and machine learning on distributed datasets"):
  with members:
    - "helen maliciously secure coopetitive learning for linear models"
    - "evaluating differentially private machine learning in practice"
    - "hotstuff bft consensus in the lens of blockchain"
    - "dp admm admm based distributed learning with differential privacy"
    - "gazelle a low latency framework for secure neural network inference"
    - "differentially private federated learning a client level perspective"
    - "towards practical differential privacy for sql queries"
    - "smcql secure querying for federated databases"

Connected through hyperedge HE-6 (induced by source paper "differentially private sql with bounded user contribution"):
  with members:
    - "sok differential privacies"
    - "detecting violations of differential privacy"
    - "shrinkwrap differentially private query processing in private data federations"
    - "renyi differential privacy"
    - "towards practical differential privacy for sql queries"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "differential privacy an estimation theory based method for choosing epsilon"
    - "a data and workload aware algorithm for range queries under differential privacy"

Connected through hyperedge HE-7 (induced by source paper "privacy preserving multiple tensor factorization for synthesizing large scale location traces"):
  with members:
    - "local obfuscation mechanisms for hiding probability distributions"
    - "on the lack of location privacy in crowdsourcing applications"
    - "the long road to computational location privacy a survey"
    - "evaluating differentially private machine learning in practice"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "fast differentially private matrix factorization"
    - "time distortion anonymization for the publication of mobility data with high utility"
    - "algorithms initializations and convergence for the nonnegative matrix factorization"

Connected through hyperedge HE-8 (induced by source paper "model checking quantitative hyperproperties"):
  with members:
    - "the density of linear time properties"
    - "datalair efficient block storage with plausible deniability against multi snapshot adversaries"
    - "the complexity of counting models of linear time temporal logic"
    - "a temporal logic for hyperproperties"
    - "quantitative information flow as safety and liveness hyperproperties"
    - "on bounding problems of quantitative information flow"

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)