human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "differentially private projected histograms of multi attribute data for classification"):
  Abstract: In this paper, we tackle the problem of constructing a differentially private synopsis for the classification analyses. Several the state-of-the-art methods follow the structure of existing classification algorithms and are all iterative, which is suboptimal due to the locally optimal choices and the over-divided privacy budget among many sequentially composed steps. Instead, we propose a new approach, PrivPfC, a new differentially private method for releasing data for classification. The key idea is to privately select an optimal partition of the underlying dataset using the given privacy budget in one step. Given one dataset and the privacy budget, PrivPfC constructs a pool of candidate grids where the number of cells of each grid is under a data-aware and privacy-budget-aware threshold. After that, PrivPfC selects an optimal grid via the exponential mechanism by using a novel quality function which minimizes the expected number of misclassified records on which a histogram classifier is constructed using the published grid. Finally, PrivPfC injects noise into each cell of the selected grid and releases the noisy grid as the private synopsis of the data. If the size of the candidate grid pool is larger than the processing capability threshold set by the data curator, we add a step in the beginning of PrivPfC to prune the set of attributes privately. We introduce a modified $\chi^2$ quality function with low sensitivity and use it to evaluate an attribute's relevance to the classification label variable. Through extensive experiments on real datasets, we demonstrate PrivPfC's superiority over the state-of-the-art methods.

Includes members:
  Member "a data and workload aware algorithm for range queries under differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "differentially private m band wavelet based mechanisms in machine learning environments")
  Member "calibrating data to sensitivity in private data analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "locally private hypothesis selection")
  Member "differentially private grids for geospatial data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentially private k means clustering with guaranteed convergence")
  Member "differentially private spatial decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "outis crypto assisted differential privacy on untrusted servers")
  Member "differential privacy via wavelet transforms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentially private m band wavelet based mechanisms in machine learning environments")
  Member "boosting the accuracy of differentially private histograms through consistency":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a workload adaptive mechanism for linear queries under local differential privacy")
  Member "differentially private empirical risk minimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")

Question: Please tell me which class the source paper 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.CR(Cryptography and Security)