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 "multi hop federated private data augmentation with sample compression"):
  Abstract: On-device machine learning (ML) has brought about the accessibility to a tremendous amount of data from the users while keeping their local data private instead of storing it in a central entity. However, for privacy guarantee, it is inevitable at each device to compensate for the quality of data or learning performance, especially when it has a non-IID training dataset. In this paper, we propose a data augmentation framework using a generative model: multi-hop federated augmentation with sample compression (MultFAug). A multi-hop protocol speeds up the end-to-end over-the-air transmission of seed samples by enhancing the transport capacity. The relaying devices guarantee stronger privacy preservation as well since the origin of each seed sample is hidden in those participants. For further privatization on the individual sample level, the devices compress their data samples. The devices sparsify their data samples prior to transmissions to reduce the sample size, which impacts the communication payload. This preprocessing also strengthens the privacy of each sample, which corresponds to the input perturbation for preserving sample privacy. The numerical evaluations show that the proposed framework significantly improves privacy guarantee, transmission delay, and local training performance with adjustment to the number of hops and compression rate.

Includes members:
  Member "decentralized learning of generative adversarial networks from multi client non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "brainstorming generative adversarial networks bgans towards multi agent generative models with distributed private datasets")
  Member "hybrid fl cooperative learning mechanism using non iid data in wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hfel joint edge association and resource allocation for cost efficient hierarchical federated edge learning")
  Member "loadaboost loss based adaboost federated machine learning on medical data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art")
  Member "communication efficient on device machine learning federated distillation and augmentation under non iid private data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "data augmentation in emotion classification using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using generative adversarial networks to synthesize artificial financial datasets")
  Member "zipnet gan inferring fine grained mobile traffic patterns via a generative adversarial neural network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cloudlstm a recurrent neural model for spatiotemporal point cloud stream forecasting")
  Member "rendergan generating realistic labeled data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cross domain few shot classification via learned feature wise transformation")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)