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: "mining frequent graph patterns with differential privacy"
  Abstract: Discovering frequent graph patterns in a graph database offers valuable information in a variety of applications. However, if the graph dataset contains sensitive data of individuals such as mobile phone-call graphs and web-click graphs, releasing discovered frequent patterns may present a threat to the privacy of individuals. {\em Differential privacy} has recently emerged as the {\em de facto} standard for private data analysis due to its provable privacy guarantee. In this paper we propose the first differentially private algorithm for mining frequent graph patterns. #R##N#We first show that previous techniques on differentially private discovery of frequent {\em itemsets} cannot apply in mining frequent graph patterns due to the inherent complexity of handling structural information in graphs. We then address this challenge by proposing a Markov Chain Monte Carlo (MCMC) sampling based algorithm. Unlike previous work on frequent itemset mining, our techniques do not rely on the output of a non-private mining algorithm. Instead, we observe that both frequent graph pattern mining and the guarantee of differential privacy can be unified into an MCMC sampling framework. In addition, we establish the privacy and utility guarantee of our algorithm and propose an efficient neighboring pattern counting technique as well. Experimental results show that the proposed algorithm is able to output frequent patterns with good precision.

Connected through hyperedge HE-1 (induced by source paper "privacy preserving link prediction with latent geometric network models"):
  with members:
    - "link prediction based on graph neural networks"
    - "struc2vec learning node representations from structural identity"
    - "node2vec scalable feature learning for networks"
    - "line large scale information network embedding"
    - "fast differentially private matrix factorization"
    - "discriminative link prediction using local links node features and community structure"
    - "calibrating data to sensitivity in private data analysis"
    - "personalized social recommendations accurate or private"

Connected through hyperedge HE-2 (induced by source paper "differentially private two party egocentric betweenness centrality"):
  with members:
    - "detecting communities under differential privacy"
    - "efficient lipschitz extensions for high dimensional graph statistics and node private degree distributions"
    - "link prediction by de anonymization how we won the kaggle social network challenge"
    - "de anonymizing social networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "cloud based approximate constrained shortest distance queries over encrypted graphs with privacy protection"):
  with members:
    - "secure fingerprint alignment and matching protocols"
    - "shortest paths and distances with differential privacy"
    - "fast exact shortest path distance queries on large networks by pruned landmark labeling"
    - "graphlab a new framework for parallel machine learning"

Connected through hyperedge HE-5 (induced by source paper "release connection fingerprints in social networks using personalized diffierential privacy"):
  with members:
    - "beating randomized response on incoherent matrices"
    - "preserving link privacy in social network based systems"

Connected through hyperedge HE-6 (induced by source paper "differential privacy and machine learning a survey and review"):
  with members:
    - "the composition theorem for 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"
    - "compressive mechanism utilizing sparse representation in 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)