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: "privtree a differentially private algorithm for hierarchical decompositions"
  Abstract: Given a set D of tuples defined on a domain Omega, we study differentially private algorithms for constructing a histogram over Omega to approximate the tuple distribution in D. Existing solutions for the problem mostly adopt a hierarchical decomposition approach, which recursively splits Omega into sub-domains and computes a noisy tuple count for each sub-domain, until all noisy counts are below a certain threshold. This approach, however, requires that we (i) impose a limit h on the recursion depth in the splitting of Omega and (ii) set the noise in each count to be proportional to h. This leads to inferior data utility due to the following dilemma: if we use a small h, then the resulting histogram would be too coarse-grained to provide an accurate approximation of data distribution; meanwhile, a large h would yield a fine-grained histogram, but its quality would be severely degraded by the increased amount of noise in the tuple counts. #R##N#To remedy the deficiency of existing solutions, we present PrivTree, a histogram construction algorithm that also applies hierarchical decomposition but features a crucial (and somewhat surprising) improvement: when deciding whether or not to split a sub-domain, the amount of noise required in the corresponding tuple count is independent of the recursive depth. This enables PrivTree to adaptively generate high-quality histograms without even asking for a pre-defined threshold on the depth of sub-domain splitting. As concrete examples, we demonstrate an application of PrivTree in modelling spatial data, and show that it can also be extended to handle sequence data (where the decision in sub-domain splitting is not based on tuple counts but a more sophisticated measure). Our experiments on a variety of real datasets show that PrivTree significantly outperforms the states of the art in terms of data utility.

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"
    - "plausible deniability for privacy preserving data synthesis"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "chasing accuracy and privacy and catching both a literature survey on differentially private histogram publication"):
  with members:
    - "privacy preserving mhealth data release with pattern consistency"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "differentially private k means clustering"
    - "local private efficient protocols for succinct histograms"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "an adaptive mechanism for accurate query answering under differential privacy"

Connected through hyperedge HE-3 (induced by source paper "privacy in trajectory micro data publishing a survey"):
  with members:
    - "towards matching user mobility traces in large scale datasets"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "where you are is who you are user identification by matching statistics"
    - "spatio temporal techniques for user identification by means of gps mobility data"
    - "tracking human mobility using wifi signals"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "protecting locations with differential privacy under temporal correlations"
    - "a data and workload aware algorithm for range queries under differential privacy"

Connected through hyperedge HE-4 (induced by source paper "modeling tabular data using conditional gan"):
  with members:
    - "data synthesis based on generative adversarial networks"
    - "generative adversarial networks for electronic health records a framework for exploring and evaluating methods for predicting drug induced laboratory test trajectories"
    - "pacgan the power of two samples in generative adversarial networks"
    - "improved training of wasserstein gans"
    - "boosting deep learning risk prediction with generative adversarial networks for electronic health records"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "on the information privacy model the group and composition privacy"):
  with members:
    - "information theory of data privacy"
    - "inferential privacy guarantees for differentially private mechanisms"
    - "calibrating data to sensitivity in private data analysis"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-6 (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"
    - "private selection from private candidates"
    - "synthesizing coupling proofs of differential privacy"
    - "laradb a minimalist kernel for linear and relational algebra computation"
    - "lightdp towards automating differential privacy proofs"
    - "differentially private bayesian programming"
    - "principled evaluation of differentially private algorithms using dpbench"

Connected through hyperedge HE-7 (induced by source paper "differentially private hierarchical count of counts histograms"):
  with members:
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "a simple and practical algorithm for differentially private data release"
    - "boosting the accuracy of differentially private histograms through consistency"
    - "differentially private empirical risk minimization"
    - "universally utility maximizing privacy mechanisms"

Connected through hyperedge HE-8 (induced by source paper "relations among different privacy notions"):
  with members:
    - "privacy loss in apple s implementation of differential privacy on macos 10 12"
    - "differentially private mixture of generative neural networks"
    - "preserving privacy enables coexistence equilibrium of competitive diffusion in social networks"
    - "composition properties of inferential privacy for time series data"
    - "adaptive laplace mechanism differential privacy preservation in deep learning"
    - "pufferfish privacy mechanisms for correlated data"
    - "concentrated differential privacy simplifications extensions and lower bounds"
    - "concentrated differential 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)