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 "privgen preserving privacy of sequences through data generation"):
  Abstract: Sequential data is everywhere, and it can serve as a basis for research that will lead to improved processes. For example, road infrastructure can be improved by identifying bottlenecks in GPS data, or early diagnosis can be improved by analyzing patterns of disease progression in medical data. The main obstacle is that access and use of such data is usually limited or not permitted at all due to concerns about violating user privacy, and rightly so. Anonymizing sequence data is not a simple task, since a user creates an almost unique signature over time. Existing anonymization methods reduce the quality of information in order to maintain the level of anonymity required. Damage to quality may disrupt patterns that appear in the original data and impair the preservation of various characteristics. Since in many cases the researcher does not need the data as is and instead is only interested in the patterns that exist in the data, we propose PrivGen, an innovative method for generating data that maintains patterns and characteristics of the source data. We demonstrate that the data generation mechanism significantly limits the risk of privacy infringement. Evaluating our method with real-world datasets shows that its generated data preserves many characteristics of the data, including the sequential model, as trained based on the source data. This suggests that the data generated by our method could be used in place of actual data for various types of analysis, maintaining user privacy and the data's integrity at the same time.

Includes members:
  Member "differentially private generative adversarial networks for time series continuous and discrete open data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dp cgan differentially private synthetic data and label generation")
  Member "privacy preserving prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "does learning require memorization a short tale about a long tail")
  Member "understanding membership inferences on well generalized learning models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dynamic backdoor attacks against machine learning models")
  Member "plausible deniability for privacy preserving data synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "leveraging hierarchical representations for preserving privacy and utility in text")
  Member "k τ e anonymity towards privacy preserving publishing of spatiotemporal trajectory data":
    also connected through other hyperedges:
      (none)
  Member "generating multi label discrete patient records using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cor gan correlation capturing convolutional neural networks for generating synthetic healthcare records")
  Member "individual differential privacy a utility preserving formulation of differential privacy guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mitigating query flooding parameter duplication attack on regression models with high dimensional gaussian mechanism")
  Member "privacy risk in machine learning analyzing the connection to overfitting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic backdoor attacks against machine learning models")

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)