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 "tighter theory for local sgd on identical and heterogeneous data"):
  Abstract: We provide a new analysis of local SGD, removing unnecessary assumptions and elaborating on the difference between two data regimes: identical and heterogeneous. In both cases, we improve the existing theory and provide values of the optimal stepsize and optimal number of local iterations. Our bounds are based on a new notion of variance that is specific to local SGD methods with different data. The tightness of our results is guaranteed by recovering known statements when we plug $H=1$, where $H$ is the number of local steps. The empirical evidence further validates the severe impact of data heterogeneity on the performance of local SGD.

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 learning of a mixture of global and local models")
  Member "variance reduced local sgd with lower communication complexity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "the error feedback framework better rates for sgd with delayed gradients and compressed communication":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "distributed optimization for over parameterized learning":
    also connected through other hyperedges:
      (none)
  Member "on the convergence of local descent methods in federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "scaffold stochastic controlled averaging for federated learning")
  Member "slowmo improving communication efficient distributed sgd with slow momentum":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overlap local sgd an algorithmic approach to hide communication delays in distributed sgd")
  Member "fedpaq a communication efficient federated learning method with periodic averaging and quantization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "personalized federated learning a meta learning approach")
  Member "first analysis of local gd on heterogeneous data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "federated learning of a mixture of global and local models")

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)