human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "federated learning of a mixture of global and local models"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "sgd general analysis and improved rates"
    - "adaptive gradient based meta learning methods"
    - "advances and open problems in federated learning"
    - "variational federated multi task learning"
    - "variance reduced local sgd with lower communication complexity"
    - "don t jump through hoops and remove those loops svrg and katyusha are better without the outer loop"
    - "semi cyclic stochastic gradient descent"

Connected through hyperedge HE-2 (induced by source paper "personalized federated learning a meta learning approach"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "adaptive gradient based meta learning methods"
    - "one shot federated learning"
    - "improving federated learning personalization via model agnostic meta learning"
    - "on the convergence of local descent methods in federated learning"
    - "alpha maml adaptive model agnostic meta learning"
    - "fedpaq a communication efficient federated learning method with periodic averaging and quantization"
    - "hyper sphere quantization communication efficient sgd for federated learning"

Connected through hyperedge HE-3 (induced by source paper "uncertainty principle for communication compression in distributed and federated learning and the search for an optimal compressor"):
  with members:
    - "scaffold stochastic controlled averaging for on device federated learning"
    - "error feedback fixes signsgd and other gradient compression schemes"
    - "powersgd practical low rank gradient compression for distributed optimization"
    - "natural compression for distributed deep learning"
    - "federated learning challenges methods and future directions"
    - "signsgd with majority vote is communication efficient and fault tolerant"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "federated learning strategies for improving communication efficiency"

Connected through hyperedge HE-4 (induced by source paper "natural compression for distributed deep learning"):
  with members:
    - "scaling distributed machine learning with in network aggregation"
    - "distributed learning with compressed gradient differences"
    - "3lc lightweight and effective traffic compression for distributed machine learning"
    - "99 of distributed optimization is a waste of time the issue and how to fix it"
    - "federated learning strategies for improving communication efficiency"
    - "not all samples are created equal deep learning with importance sampling"
    - "deep gradient compression reducing the communication bandwidth for distributed training"
    - "gradient sparsification for communication efficient distributed optimization"

Question: Please tell me which class the center node 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)