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: "differential privacy an economic method for choosing epsilon"
  Abstract: Differential privacy is becoming a gold standard for privacy research; it offers a guaranteed bound on loss of privacy due to release of query results, even under worst-case assumptions. The theory of differential privacy is an active research area, and there are now differentially private algorithms for a wide range of interesting problems. #R##N#However, the question of when differential privacy works in practice has received relatively little attention. In particular, there is still no rigorous method for choosing the key parameter $\epsilon$, which controls the crucial tradeoff between the strength of the privacy guarantee and the accuracy of the published results. #R##N#In this paper, we examine the role that these parameters play in concrete applications, identifying the key questions that must be addressed when choosing specific values. This choice requires balancing the interests of two different parties: the data analyst and the prospective participant, who must decide whether to allow their data to be included in the analysis. We propose a simple model that expresses this balance as formulas over a handful of parameters, and we use our model to choose $\epsilon$ on a series of simple statistical studies. We also explore a surprising insight: in some circumstances, a differentially private study can be more accurate than a non-private study for the same cost, under our model. Finally, we discuss the simplifying assumptions in our model and outline a research agenda for possible refinements.

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 "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism"):
  with members:
    - "mvg mechanism differential privacy under matrix valued query"
    - "stealing neural networks via timing side channels"
    - "prada protecting against dnn model stealing attacks"
    - "utility optimized local differential privacy mechanisms for distribution estimation"
    - "stealing hyperparameters in machine learning"
    - "model extraction warning in mlaas paradigm"
    - "individual differential privacy a utility preserving formulation of differential privacy guarantees"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-3 (induced by source paper "differential privacy at risk bridging randomness and privacy budget"):
  with members:
    - "federated learning with bayesian differential privacy"
    - "issues encountered deploying differential privacy"
    - "pain free random differential privacy with sensitivity sampling"
    - "concentrated differential privacy"
    - "functional mechanism regression analysis under differential privacy"
    - "truthful mechanisms for agents that value privacy"
    - "selling privacy at auction"
    - "differentially private empirical risk minimization"

Connected through hyperedge HE-4 (induced by source paper "data dependent differentially private parameter learning for directed graphical models"):
  with members:
    - "data dependent pac bayes priors via differential privacy"
    - "differentially private empirical risk minimization revisited faster and more general"
    - "differentially private bayesian inference for exponential families"
    - "differentially private learning of undirected graphical models using collective graphical models"
    - "dp em differentially private expectation maximization"
    - "on the theory and practice of privacy preserving bayesian data analysis"
    - "understanding deep learning requires rethinking generalization"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"

Connected through hyperedge HE-5 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "renyi differential privacy"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-6 (induced by source paper "outis crypto assisted differential privacy on untrusted servers"):
  with members:
    - "an economic analysis of privacy protection and statistical accuracy as social choices"
    - "mvg mechanism differential privacy under matrix valued query"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "ektelo a framework for defining differentially private computations"
    - "shrinkwrap differentially private query processing in private data federations"
    - "locally differentially private heavy hitter identification"
    - "marginal release under local differential privacy"
    - "cryptodl deep neural networks over encrypted data"

Connected through hyperedge HE-7 (induced by source paper "effects of differential privacy and data skewness on membership inference vulnerability"):
  with members:
    - "demystifying learning rate polices for high accuracy training of deep neural networks"
    - "cross layer strategic ensemble defense against adversarial examples"
    - "differentially private model publishing for deep learning"
    - "evaluating differentially private machine learning in practice"
    - "robust or private adversarial training makes models more vulnerable to privacy attacks"
    - "deep neural network ensembles against deception ensemble diversity accuracy and robustness"
    - "exploiting unintended feature leakage in collaborative learning"
    - "beyond inferring class representatives user level privacy leakage from federated learning"

Connected through hyperedge HE-8 (induced by source paper "blender enabling local search with a hybrid differential privacy model"):
  with members:
    - "calibrate frequency estimation and heavy hitter identification with local differential privacy via incorporating prior knowledge"
    - "privacy amplification by iteration"
    - "the power of the hybrid model for mean estimation"
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "practical locally private heavy hitters"
    - "differentially private identity and closeness testing of discrete distributions"
    - "differentially private testing of identity and closeness of discrete distributions"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"

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.DB(Databases)