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 "asynchronous federated learning with differential privacy for edge intelligence"):
  Abstract: Federated learning has been showing as a promising approach in paving the last mile of artificial intelligence, due to its great potential of solving the data isolation problem in large scale machine learning. Particularly, with consideration of the heterogeneity in practical edge computing systems, asynchronous edge-cloud collaboration based federated learning can further improve the learning efficiency by significantly reducing the straggler effect. Despite no raw data sharing, the open architecture and extensive collaborations of asynchronous federated learning (AFL) still give some malicious participants great opportunities to infer other parties' training data, thus leading to serious concerns of privacy. To achieve a rigorous privacy guarantee with high utility, we investigate to secure asynchronous edge-cloud collaborative federated learning with differential privacy, focusing on the impacts of differential privacy on model convergence of AFL. Formally, we give the first analysis on the model convergence of AFL under DP and propose a multi-stage adjustable private algorithm (MAPA) to improve the trade-off between model utility and privacy by dynamically adjusting both the noise scale and the learning rate. Through extensive simulations and real-world experiments with an edge-could testbed, we demonstrate that MAPA significantly improves both the model accuracy and convergence speed with sufficient privacy guarantee.

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 "min entropy latent model for weakly supervised object detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "weakly supervised instance segmentation by deep community learning")
  Member "federated machine learning concept and applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the future of digital health with federated learning")
  Member "interpret federated learning with shapley values":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "measure contribution of participants in federated learning")
  Member "exploiting unintended feature leakage in collaborative learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the convergence of artificial intelligence and distributed ledger technology a scoping review and future research agenda")
  Member "mvg mechanism differential privacy under matrix valued query":
    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 "trunk branch ensemble convolutional neural networks for video based face recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dual triplet metric learning for unsupervised domain adaptation in video based face recognition")
  Member "federated learning strategies for improving communication efficiency":
    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.LG(Machine Learning)