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 "ternary compression for communication efficient federated learning"):
  Abstract: Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and IoT devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. A convergence proof of the quantization factor and the unbiasedness of FTTQ is given. In addition, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available datasets, and our results demonstrate the effectiveness of FTTQ and T-FedAvg compared with the canonical federated learning algorithms in reducing communication costs and maintaining the learning performance.

Includes members:
  Member "a survey on federated learning systems vision hype and reality for data privacy and protection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to detect malicious clients for robust federated learning")
  Member "towards federated learning at scale system design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "do we really need to access the source data source hypothesis transfer for unsupervised domain adaptation")
  Member "robust and communication efficient federated learning from non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficient federated edge learning with joint communication and computation design")
  Member "federated machine learning concept and applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "the future of digital health with federated learning")
  Member "federated learning in mobile edge networks a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art")
  Member "model compression via distillation and quantization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "adaptive federated learning in resource constrained edge computing systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "multi objective evolutionary federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "real time federated evolutionary neural architecture search")

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)