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: "a framework for high accuracy privacy preserving mining"
  Abstract: To preserve client privacy in the data mining process, a variety of techniques based on random perturbation of data records have been proposed recently. In this paper, we present a generalized matrix-theoretic model of random perturbation, which facilitates a systematic approach to the design of perturbation mechanisms for privacy-preserving mining. Specifically, we demonstrate that (a) the prior techniques differ only in their settings for the model parameters, and (b) through appropriate choice of parameter settings, we can derive new perturbation techniques that provide highly accurate mining results even under strict privacy guarantees. We also propose a novel perturbation mechanism wherein the model parameters are themselves characterized as random variables, and demonstrate that this feature provides significant improvements in privacy at a very marginal cost in accuracy. #R##N#While our model is valid for random-perturbation-based privacy-preserving mining in general, we specifically evaluate its utility here with regard to frequent-itemset mining on a variety of real datasets. The experimental results indicate that our mechanisms incur substantially lower identity and support errors as compared to the prior techniques.

Connected through hyperedge HE-1 (induced by source paper "an efficient and scalable privacy preserving algorithm for big data and data streams"):
  with members:
    - "efficient privacy preservation of big data for accurate data mining"
    - "efficient data perturbation for privacy preserving and accurate data stream mining"
    - "benchmarking distributed stream processing platforms for iot applications"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "extremal mechanisms for local differential privacy"
    - "building confidential and efficient query services in the cloud with rasp data perturbation"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-2 (induced by source paper "the price of differential privacy for low rank factorization"):
  with members:
    - "communication efficient algorithms for distributed stochastic principal component analysis"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "optimal principal component analysis in distributed and streaming models"
    - "wishart mechanism for differentially private principal components analysis"
    - "sketching as a tool for numerical linear algebra"
    - "differentially private linear algebra in the streaming model"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-3 (induced by source paper "local private efficient protocols for succinct histograms"):
  with members:
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "rappor randomized aggregatable privacy preserving ordinal response"
    - "privacy via the johnson lindenstrauss transform"
    - "distributed private heavy hitters"
    - "calibrating data to sensitivity in private data analysis"
    - "the johnson lindenstrauss transform itself preserves differential privacy"
    - "what can we learn privately"

Connected through hyperedge HE-4 (induced by source paper "a framework for extracting semantic guarantees from privacy"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "what can we learn privately"
    - "universally utility maximizing privacy mechanisms"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-5 (induced by source paper "publishing microdata with a robust privacy guarantee"):
  with members:
    - "transparent anonymization thwarting adversaries who know the algorithm"
    - "composition attacks and auxiliary information in data privacy"

Connected through hyperedge HE-6 (induced by source paper "on sampling anonymization and differential privacy or k anonymization meets differential privacy"):
  with members:
    - "calibrating data to sensitivity in private data analysis"
    - "a learning theory approach to non interactive database privacy"
    - "privacy in search logs"
    - "what can we learn privately"

Connected through hyperedge HE-7 (induced by source paper "what can we learn privately"):
  with members:
    - "almost everywhere algorithmic stability and generalization error"
    - "a learning theory approach to non interactive database privacy"
    - "efficient differentially private point estimators"
    - "cryptographic randomized response techniques"
    - "noise tolerant learning the parity problem and the statistical query model"

Connected through hyperedge HE-8 (induced by source paper "composition attacks and auxiliary information in data privacy"):
  with members:
    - "a learning theory approach to non interactive database privacy"
    - "worst case background knowledge for privacy preserving data publishing"

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)