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 "anonymizing data for privacy preserving federated learning"):
  Abstract: Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensitive information, and data analysis methods must provably comply with regulatory guidelines. Although federated learning prevents sharing raw data, it is still possible to launch privacy attacks on the model parameters that are exposed during the training process, or on the generated machine learning model. In this paper, we propose the first syntactic approach for offering privacy in the context of federated learning. Unlike the state-of-the-art differential privacy-based frameworks, our approach aims to maximize utility or model performance, while supporting a defensible level of privacy, as demanded by GDPR and HIPAA. We perform a comprehensive empirical evaluation on two important problems in the healthcare domain, using real-world electronic health data of 1 million patients. The results demonstrate the effectiveness of our approach in achieving high model performance, while offering the desired level of privacy. Through comparative studies, we also show that, for varying datasets, experimental setups, and privacy budgets, our approach offers higher model performance than differential privacy-based techniques in federated learning.

Includes members:
  Member "differentially private learning with adaptive clipping":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "removing disparate impact of differentially private stochastic gradient descent on model accuracy")
  Member "differential privacy enabled federated learning for sensitive health data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "wireless federated learning with local differential privacy")
  Member "beyond inferring class representatives user level privacy leakage from federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "a hybrid approach to privacy preserving federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "adaptive federated learning in resource constrained edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "a general approach to adding differential privacy to iterative training procedures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "how to backdoor federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trends and advancements in deep neural network communication")
  Member "differentially private federated learning a client level perspective":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "trends and advancements in deep neural network communication")

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)