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: "compressive mechanism utilizing sparse representation in differential privacy"
  Abstract: Differential privacy provides the first theoretical foundation with provable privacy guarantee against adversaries with arbitrary prior knowledge. The main idea to achieve differential privacy is to inject random noise into statistical query results. Besides correctness, the most important goal in the design of a differentially private mechanism is to reduce the effect of random noise, ensuring that the noisy results can still be useful.   This paper proposes the compressive mechanism, a novel solution on the basis of state-of-the-art compression technique, called compressive sensing. Compressive sensing is a decent theoretical tool for compact synopsis construction, using random projections. In this paper, we show that the amount of noise is significantly reduced from O(n) to O(log(n)), when the noise insertion procedure is carried on the synopsis samples instead of the original database. As an extension, we also apply the proposed compressive mechanism to solve the problem of continual release of statistical results. Extensive experiments using real datasets justify our accuracy claims.

Connected through hyperedge HE-1 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices"):
  with members:
    - "indistinguishability and energy sensitivity of asymptotically gaussian compressed encryption"
    - "a survey on wireless security technical challenges recent advances and future trends"
    - "bi level protected compressive sampling"
    - "on known plaintext attacks to a compressed sensing based encryption a quantitative analysis"
    - "low complexity multiclass encryption by compressed sensing"
    - "mergeshuffle a very fast parallel random permutation algorithm"
    - "permutation meets parallel compressed sensing how to relax restricted isometry property for 2d sparse signals"
    - "a compressed sensing wire tap channel"

Connected through hyperedge HE-2 (induced by source paper "differential privacy techniques for cyber physical systems a survey"):
  with members:
    - "quantifying differential privacy in continuous data release under temporal correlations"
    - "sok differential privacies"
    - "improving the gaussian mechanism for differential privacy analytical calibration and optimal denoising"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "achieving differential privacy against non intrusive load monitoring in smart grid a fog computing approach"
    - "toward practical privacy preserving analytics for iot and cloud based healthcare systems"
    - "privacy preserving distributed deep learning for clinical data"
    - "re dpoctor real time health data releasing with w day differential privacy"

Connected through hyperedge HE-3 (induced by source paper "mvg mechanism differential privacy under matrix valued query"):
  with members:
    - "a differential privacy mechanism design under matrix valued query"
    - "towards practical differential privacy for sql queries"
    - "coupling random orthonormal projection with gaussian generative model for non interactive private data release"
    - "convex optimization for linear query processing under approximate differential privacy"
    - "understanding the sparse vector technique for differential privacy"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "optimizing batch linear queries under exact and approximate differential privacy"
    - "an improved private mechanism for small databases"

Connected through hyperedge HE-4 (induced by source paper "ron gauss enhancing utility in non interactive private data release"):
  with members:
    - "plausible deniability for privacy preserving data synthesis"
    - "differential privacy as a mutual information constraint"
    - "membership inference attacks against machine learning models"
    - "privacy protection for natural language records neural generative models for releasing synthetic twitter data"
    - "unsupervised deep embedding for clustering analysis"
    - "building a rappor with the unknown privacy preserving learning of associations and data dictionaries"
    - "principled evaluation of differentially private algorithms using dpbench"
    - "protecting locations with differential privacy under temporal correlations"

Connected through hyperedge HE-5 (induced by source paper "privacy preserving social media data publishing"):
  with members:
    - "linkmirage how to anonymize links in dynamic social systems"
    - "your actions tell where you are uncovering twitter users in a metropolitan area"
    - "geo indistinguishability differential privacy for location based systems"
    - "calibrating data to sensitivity in private data analysis"
    - "preserving link privacy in social network based systems"
    - "a learning theory approach to non interactive database privacy"
    - "differential privacy via wavelet transforms"
    - "de anonymizing social networks"

Connected through hyperedge HE-6 (induced by source paper "local differential privacy for physical sensor data and sparse recovery"):
  with members:
    - "lopub high dimensional crowdsourced data publication with local differential privacy"
    - "optimal noise adding mechanisms for approximate differential privacy"
    - "fingerprinting codes and the price of approximate differential privacy"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-7 (induced by source paper "optimizing batch linear queries under exact and approximate differential privacy"):
  with members:
    - "the geometry of differential privacy the sparse and approximate cases"
    - "calibrating data to sensitivity in private data analysis"
    - "privbasis frequent itemset mining with differential privacy"
    - "an adaptive mechanism for accurate query answering under differential privacy"
    - "low rank mechanism for optimizing batch queries under differential privacy"
    - "beating randomized response on incoherent matrices"
    - "optimal error of query sets under the differentially private matrix mechanism"
    - "functional mechanism regression analysis under differential privacy"

Connected through hyperedge HE-8 (induced by source paper "differential privacy and machine learning a survey and review"):
  with members:
    - "the composition theorem for differential privacy"
    - "mining frequent graph patterns with differential privacy"
    - "calibrating data to sensitivity in private data analysis"
    - "convergence rates for differentially private statistical estimation"
    - "beyond worst case analysis in private singular vector computation"
    - "functional mechanism regression analysis under differential privacy"
    - "differentially private online learning"
    - "a learning theory approach to non interactive database 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.DS(Data Structures and Algorithms)