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: "differentially private publication of sparse data"
  Abstract: The problem of privately releasing data is to provide a version of a dataset without revealing sensitive information about the individuals who contribute to the data. The model of differential privacy allows such private release while providing strong guarantees on the output. A basic mechanism achieves differential privacy by adding noise to the frequency counts in the contingency tables (or, a subset of the count data cube) derived from the dataset. However, when the dataset is sparse in its underlying space, as is the case for most multi-attribute relations, then the effect of adding noise is to vastly increase the size of the published data: it implicitly creates a huge number of dummy data points to mask the true data, making it almost impossible to work with. #R##N#We present techniques to overcome this roadblock and allow efficient private release of sparse data, while maintaining the guarantees of differential privacy. Our approach is to release a compact summary of the noisy data. Generating the noisy data and then summarizing it would still be very costly, so we show how to shortcut this step, and instead directly generate the summary from the input data, without materializing the vast intermediate noisy data. We instantiate this outline for a variety of sampling and filtering methods, and show how to use the resulting summary for approximate, private, query answering. Our experimental study shows that this is an effective, practical solution, with comparable and occasionally improved utility over the costly materialization approach.

Connected through hyperedge HE-1 (induced by source paper "privacy for all demystify vulnerability disparity of differential privacy against membership inference attack"):
  with members:
    - "memguard defending against black box membership inference attacks via adversarial examples"
    - "evaluating differentially private machine learning in practice"
    - "exploiting unintended feature leakage in collaborative learning"
    - "gids gan based intrusion detection system for in vehicle network"
    - "the natural auditor how to tell if someone used your words to train their model"
    - "counterfactual fairness in text classification through robustness"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"

Connected through hyperedge HE-2 (induced by source paper "mobilitymirror bias adjusted transportation datasets"):
  with members:
    - "bias in olap queries detection explanation and removal"
    - "fairness testing testing software for discrimination"
    - "discrimination in online ad delivery"
    - "calibrating data to sensitivity in private data analysis"
    - "dpcube differentially private histogram release through multidimensional partitioning"
    - "differential privacy via wavelet transforms"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "universally utility maximizing privacy mechanisms"

Connected through hyperedge HE-3 (induced by source paper "functional mechanism regression analysis under differential privacy"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private spatial decompositions"
    - "compressive mechanism utilizing sparse representation in differential privacy"
    - "differential privacy via wavelet transforms"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "differentially private empirical risk minimization"
    - "learning in a large function space privacy preserving mechanisms for svm learning"

Connected through hyperedge HE-4 (induced by source paper "a propagation model for provenance views of public private workflows"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "surrogate parenthood protected and informative graphs"
    - "transparent anonymization thwarting adversaries who know the algorithm"
    - "provenance views for module privacy"
    - "differential privacy via wavelet transforms"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-5 (induced by source paper "compressive mechanism utilizing sparse representation in differential privacy"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "compressive acquisition of dynamic scenes"
    - "lower bounds in differential privacy"
    - "differential privacy via wavelet transforms"
    - "on the geometry of differential privacy"
    - "interactive privacy via the median mechanism"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "universally utility maximizing privacy mechanisms"

Connected through hyperedge HE-6 (induced by source paper "differentially private spatial decompositions"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "a learning theory approach to non interactive database privacy"
    - "differential privacy via wavelet transforms"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "what can we learn privately"
    - "universally utility maximizing privacy mechanisms"

Connected through hyperedge HE-7 (induced by source paper "differentially private trajectory data publication"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "a learning theory approach to non interactive database privacy"
    - "differential privacy via wavelet transforms"
    - "interactive privacy via the median mechanism"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "composition attacks and auxiliary information in data 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.DB(Databases)