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 via wavelet transforms"
  Abstract: Privacy preserving data publishing has attracted considerable research interest in recent years. Among the existing solutions, {\em $\epsilon$-differential privacy} provides one of the strongest privacy guarantees. Existing data publishing methods that achieve $\epsilon$-differential privacy, however, offer little data utility. In particular, if the output dataset is used to answer count queries, the noise in the query answers can be proportional to the number of tuples in the data, which renders the results useless. #R##N#In this paper, we develop a data publishing technique that ensures $\epsilon$-differential privacy while providing accurate answers for {\em range-count queries}, i.e., count queries where the predicate on each attribute is a range. The core of our solution is a framework that applies {\em wavelet transforms} on the data before adding noise to it. We present instantiations of the proposed framework for both ordinal and nominal data, and we provide a theoretical analysis on their privacy and utility guarantees. In an extensive experimental study on both real and synthetic data, we show the effectiveness and efficiency of our solution.

Connected through hyperedge HE-1 (induced by source paper "differentially private m band wavelet based mechanisms in machine learning environments"):
  with members:
    - "benefits and pitfalls of the exponential mechanism with applications to hilbert spaces and functional pca"
    - "pain free random differential privacy with sensitivity sampling"
    - "a data and workload aware algorithm for range queries under differential privacy"
    - "differential privacy and machine learning a survey and review"
    - "boosting the accuracy of differentially private histograms through consistency"

Connected through hyperedge HE-2 (induced by source paper "on the power of multiple anonymous messages"):
  with members:
    - "improved summation from shuffling"
    - "separating local shuffled differential privacy via histograms"
    - "the privacy blanket of the shuffle model"
    - "differentially private summation with multi message shuffling"
    - "practical and robust privacy amplification with multi party differential privacy"
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "private aggregation from fewer anonymous messages"
    - "scalable and differentially private distributed aggregation in the shuffled model"

Connected through hyperedge HE-3 (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-4 (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-5 (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-6 (induced by source paper "private heavy hitters and range queries in the shuffled model"):
  with members:
    - "communication complexity in locally private distribution estimation and heavy hitters"
    - "scalable and differentially private distributed aggregation in the shuffled model"
    - "tight lower bounds for locally differentially private selection"
    - "answering range queries under local differential privacy"
    - "practical locally private heavy hitters"
    - "marginal release under local differential privacy"
    - "heavy hitters and the structure of local privacy"
    - "prochlo strong privacy for analytics in the crowd"

Connected through hyperedge HE-7 (induced by source paper "linear and range counting under metric based local differential privacy"):
  with members:
    - "the privacy blanket of the shuffle model"
    - "secure and utility aware data collection with condensed local differential privacy"
    - "consistent and accurate frequency oracles under local differential privacy"
    - "capacity bounded differential privacy"
    - "hadamard response estimating distributions privately efficiently and with little communication"
    - "amplification by shuffling from local to central differential privacy via anonymity"
    - "distributed differential privacy via shuffling"
    - "utility optimized local differential privacy mechanisms for distribution estimation"

Connected through hyperedge HE-8 (induced by source paper "a programming framework for differential privacy with accuracy concentration bounds"):
  with members:
    - "fuzzi a three level logic for differential privacy"
    - "proving differential privacy with shadow execution"
    - "ektelo a framework for defining differentially private computations"
    - "synthesizing coupling proofs of differential privacy"
    - "apex accuracy aware differentially private data exploration"
    - "renyi differential privacy"
    - "lightdp towards automating differential privacy proofs"
    - "advanced probabilistic couplings for 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)