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 "federated clustering via matrix factorization models from model averaging to gradient sharing"):
  Abstract: Recently, federated learning (FL) has drawn significant attention due to its capability of training a model over the network without knowing the client's private raw data. In this paper, we study the unsupervised clustering problem under the FL setting. By adopting a generalized matrix factorization model for clustering, we propose two novel (first-order) federated clustering (FedC) algorithms based on principles of model averaging and gradient sharing, respectively, and present their theoretical convergence conditions. We show that both algorithms have a $\mathcal{O}(1/T)$ convergence rate, where $T$ is the total number of gradient evaluations per client, and the communication cost can be effectively reduced by controlling the local epoch length and allowing partial client participation within each communication round. Numerical experiments show that the FedC algorithm based on gradient sharing outperforms that based on model averaging, especially in scenarios with non-i.i.d. data, and can perform comparably as or exceed the centralized clustering algorithms.

Includes members:
  Member "privacy preserving point of interest recommendation using decentralized matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practical privacy preserving poi recommendation")
  Member "agnostic federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "clustering by orthogonal nmf model and non convex penalty optimization":
    also connected through other hyperedges:
      (none)
  Member "secure federated matrix factorization":
    also connected through other hyperedges:
      (none)
  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 "efficient decentralized deep learning by dynamic model averaging":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "trends and advancements in deep neural network communication")
  Member "learning from hidden traits joint factor analysis and latent clustering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "clustering by orthogonal nmf model and non convex penalty optimization")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)