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 "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism"):
  Abstract: Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries. Differential privacy (DP) has been considered a promising technique to mitigate this attack. However, we find that the vulnerability persists when regression models are being protected by current DP solutions. We show that the adversary can launch a query-flooding parameter duplication (QPD) attack to infer the model information by repeated queries. #R##N#To defend against the QPD attack on logistic and linear regression models, we propose a novel High-Dimensional Gaussian (HDG) mechanism to prevent unauthorized information disclosure without interrupting the intended services. In contrast to prior work, the proposed HDG mechanism will dynamically generate the privacy budget and random noise for different queries and their results to enhance the obfuscation. Besides, for the first time, HDG enables an optimal privacy budget allocation that automatically determines the minimum amount of noise to be added per user-desired privacy level on each dimension. We comprehensively evaluate the performance of HDG using real-world datasets and shows that HDG effectively mitigates the QPD attack while satisfying the privacy requirements. We also prepare to open-source the relevant codes to the community for further research.

Includes members:
  Member "mvg mechanism differential privacy under matrix valued query":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "crypt epsilon crypto assisted differential privacy on untrusted servers")
  Member "stealing neural networks via timing side channels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how to 0wn nas in your spare time")
  Member "prada protecting against dnn model stealing attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nass optimizing secure inference via neural architecture search")
  Member "utility optimized local differential privacy mechanisms for distribution estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linear and range counting under metric based local differential privacy")
  Member "stealing hyperparameters in machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "model extraction warning in mlaas paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "model extraction attacks against recurrent neural networks")
  Member "individual differential privacy a utility preserving formulation of differential privacy guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "privgen preserving privacy of sequences through data generation")
  Member "practical black box attacks against machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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)