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: "towards practical differential privacy for sql queries"
  Abstract: Differential privacy promises to enable general data analytics while protecting individual privacy, but existing differential privacy mechanisms do not support the wide variety of features and databases used in real-world SQL-based analytics systems. #R##N#This paper presents the first practical approach for differential privacy of SQL queries. Using 8.1 million real-world queries, we conduct an empirical study to determine the requirements for practical differential privacy, and discuss limitations of previous approaches in light of these requirements. To meet these requirements we propose elastic sensitivity, a novel method for approximating the local sensitivity of queries with general equijoins. We prove that elastic sensitivity is an upper bound on local sensitivity and can therefore be used to enforce differential privacy using any local sensitivity-based mechanism. #R##N#We build FLEX, a practical end-to-end system to enforce differential privacy for SQL queries using elastic sensitivity. We demonstrate that FLEX is compatible with any existing database, can enforce differential privacy for real-world SQL queries, and incurs negligible (0.03%) performance overhead.

Connected through hyperedge HE-1 (induced by source paper "linkedin s audience engagements api a privacy preserving data analytics system at scale"):
  with members:
    - "practical differentially private top k selection with pay what you get composition"
    - "optimal differential privacy composition for exponential mechanisms and the cost of adaptivity"
    - "pripearl a framework for privacy preserving analytics and reporting at linkedin"
    - "collecting telemetry data privately"
    - "psi ψ a private data sharing interface"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "the composition theorem for differential privacy"
    - "calibrating data to sensitivity in private data analysis"

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"
    - "synthesizing coupling proofs of differential privacy"
    - "renyi differential privacy"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-3 (induced by source paper "duet an expressive higher order language and linear type system for statically enforcing differential privacy"):
  with members:
    - "fuzzi a three level logic for differential privacy"
    - "approximate span liftings compositional semantics for relaxations of differential privacy"
    - "bidirectional type checking for relational properties"
    - "probabilistic relational reasoning via metrics"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "renyi differential privacy"
    - "a semantic account of metric preservation"
    - "lightdp towards automating differential privacy proofs"

Connected through hyperedge HE-4 (induced by source paper "er ae differentially private text generation for authorship anonymization"):
  with members:
    - "author obfuscation using generalised differential privacy"
    - "unsupervised text style transfer using language models as discriminators"
    - "adversarial text generation via feature mover s distance"
    - "universal sentence encoder"
    - "content preserving text generation with attribute controls"
    - "a dataset of peer reviews peerread collection insights and nlp applications"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "syntf synthetic and differentially private term frequency vectors for privacy preserving text mining"

Connected through hyperedge HE-5 (induced by source paper "local differential privacy for deep learning"):
  with members:
    - "efficient privacy preservation of big data for accurate data mining"
    - "an energy driven network function virtualization for multi domain software defined networks"
    - "an efficient and scalable privacy preserving algorithm for big data and data streams"
    - "mvg mechanism differential privacy under matrix valued query"
    - "efficient data perturbation for privacy preserving and accurate data stream mining"
    - "a hybrid deep learning architecture for privacy preserving mobile analytics"
    - "machine learning models that remember too much"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (induced by source paper "the power of factorization mechanisms in local and central differential privacy"):
  with members:
    - "differentially private sql with bounded user contribution"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "towards instance optimal private query release"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"
    - "an improved private mechanism for small databases"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "faster private release of marginals on small databases"

Connected through hyperedge HE-7 (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"
    - "synthesizing coupling proofs of differential privacy"
    - "relational symbolic execution"
    - "renyi differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for differential privacy"

Connected through hyperedge HE-8 (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"
    - "synthesizing coupling proofs of differential privacy"
    - "learning differentially private recurrent language models"
    - "lightdp towards automating differential privacy proofs"
    - "probabilistic couplings for probabilistic reasoning"
    - "psi ψ a private data sharing interface"

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)