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 learning of a mixture of global and local models"):
  Abstract: We propose a new optimization formulation for training federated learning models. The standard formulation has the form of an empirical risk minimization problem constructed to find a single global model trained from the private data stored across all participating devices. In contrast, our formulation seeks an explicit trade-off between this traditional global model and the local models, which can be learned by each device from its own private data without any communication. Further, we develop several efficient variants of SGD (with and without partial participation and with and without variance reduction) for solving the new formulation and prove communication complexity guarantees. Notably, our methods are similar but not identical to federated averaging / local SGD, thus shedding some light on the essence of the elusive method. In particular, our methods do not perform full averaging steps and instead merely take steps towards averaging. We argue for the benefits of this new paradigm for federated learning.

Includes members:
  Member "scaffold stochastic controlled averaging for on device federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "federated visual classification with real world data distribution")
  Member "sgd general analysis and improved rates":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")
  Member "adaptive gradient based meta learning methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "survey of personalization techniques for federated learning")
  Member "advances and open problems in federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "trends and advancements in deep neural network communication")
  Member "tighter theory for local sgd on identical and heterogeneous data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "personalized federated learning a meta learning approach")
  Member "variational federated multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dynamic federated learning")
  Member "variance reduced local sgd with lower communication complexity":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptivity of stochastic gradient methods for nonconvex optimization")

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)