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 "a federated learning framework for privacy preserving and parallel training"):
  Abstract: The deployment of such deep learning in practice has been hurdled by two issues: the computational cost of model training and the privacy issue of training data such as medical or healthcare records. The large size of both learning models and datasets incurs a massive computational cost, requiring efficient approaches to speed up the training phase. While parallel and distributed learning can address the issue of computational overhead, preserving the privacy of training data and intermediate results (e.g., gradients) remains a hard problem. Enabling parallel training of deep learning models on distributed datasets while preserving data privacy is even more complex and challenging. In this paper, we develop and implement FEDF, a distributed deep learning framework for privacy-preserving and parallel training. The framework allows a model to be learned on multiple geographically-distributed training datasets (which may belong to different owners) while do not reveal any information of each dataset as well as the intermediate results. We formally prove the convergence of the learning model when training with the developed framework and its privacy-preserving property. We carry out extensive experiments to evaluate the performance of the framework in terms of speedup ratio, the approximation to the upper-bound performance (when training centrally) and communication overhead between the master and training workers. The results show that the developed framework achieves a speedup of up to 9x compared to the centralized training approach and maintaining the performance approximation of the models within 4.5% of the centrally-trained models. The proposed framework also significantly reduces the amount of data exchanged between the master and training workers by up to 34% compared to existing work.

Includes members:
  Member "privacy preserving deep learning via weight transmission":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "abnormal client behavior detection in federated learning")
  Member "towards fair and decentralized privacy preserving deep learning with blockchain":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "threats to federated learning a survey")
  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 "demystifying parallel and distributed deep learning an in depth concurrency analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "communication optimization strategies for distributed deep learning a survey")
  Member "beyond data and model parallelism for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "simulating performance of ml systems with offline profiling")
  Member "optimize deep convolutional neural network with ternarized weights and high accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "simultaneously optimizing weight and quantizer of ternary neural network using truncated gaussian approximation")
  Member "privacy preserving distributed deep learning for clinical data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "deep gradient compression reducing the communication bandwidth for distributed training":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sparse weight activation training")

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.DC(Distributed, Parallel, and Cluster Computing)