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: "private selection from private candidates"
  Abstract: Differentially Private algorithms often need to select the best amongst many candidate options. Classical works on this selection problem require that the candidates' goodness, measured as a real-valued score function, does not change by much when one person's data changes. In many applications such as hyperparameter optimization, this stability assumption is much too strong. In this work, we consider the selection problem under a much weaker stability assumption on the candidates, namely that the score functions are differentially private. Under this assumption, we present algorithms that are near-optimal along the three relevant dimensions: privacy, utility and computational efficiency. #R##N#Our result can be seen as a generalization of the exponential mechanism and its existing generalizations. We also develop an online version of our algorithm, that can be seen as a generalization of the sparse vector technique to this weaker stability assumption. We show how our results imply better algorithms for hyperparameter selection in differentially private machine learning, as well as for adaptive data analysis.

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"
    - "ektelo a framework for defining differentially private computations"
    - "detecting violations of differential privacy"
    - "how to use heuristics for differential privacy"
    - "synthesizing coupling proofs of differential privacy"

Connected through hyperedge HE-2 (induced by source paper "differentially private mixed type data generation for unsupervised learning"):
  with members:
    - "dp cgan differentially private synthetic data and label generation"
    - "differentially private learning with adaptive clipping"
    - "differentially private model publishing for deep learning"
    - "differentially private generative adversarial networks for time series continuous and discrete open data"
    - "differentially private generative adversarial network"
    - "differentially private data generative models"
    - "generating artificial data for private deep learning"
    - "differentially private mixture of generative neural networks"

Connected through hyperedge HE-3 (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"
    - "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"
    - "towards practical differential privacy for sql queries"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-4 (induced by source paper "differentially private learning with adaptive clipping"):
  with members:
    - "exploiting unintended feature leakage in collaborative learning"
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "a general approach to adding differential privacy to iterative training procedures"
    - "learning differentially private recurrent language models"
    - "deep learning techniques for music generation a survey"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "communication efficient learning of deep networks from decentralized data"
    - "membership inference attacks against machine learning models"

Connected through hyperedge HE-5 (induced by source paper "ektelo a framework for defining differentially private computations"):
  with members:
    - "graphical model based estimation and inference for differential privacy"
    - "optimizing error of high dimensional statistical queries under differential privacy"
    - "synthesizing coupling proofs of differential privacy"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "lightdp towards automating differential privacy proofs"
    - "privtree a differentially private algorithm for hierarchical decompositions"
    - "differentially private bayesian programming"
    - "principled evaluation of differentially private algorithms using dpbench"

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.DS(Data Structures and Algorithms)