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 with non iid data"):
  Abstract: Federated learning enables resource-constrained edge compute devices, such as mobile phones and IoT devices, to learn a shared model for prediction, while keeping the training data local. This decentralized approach to train models provides privacy, security, regulatory and economic benefits. In this work, we focus on the statistical challenge of federated learning when local data is non-IID. We first show that the accuracy of federated learning reduces significantly, by up to 55% for neural networks trained for highly skewed non-IID data, where each client device trains only on a single class of data. We further show that this accuracy reduction can be explained by the weight divergence, which can be quantified by the earth mover's distance (EMD) between the distribution over classes on each device and the population distribution. As a solution, we propose a strategy to improve training on non-IID data by creating a small subset of data which is globally shared between all the edge devices. Experiments show that accuracy can be increased by 30% for the CIFAR-10 dataset with only 5% globally shared data.

Includes members:
  Member "federated learning strategies for improving communication efficiency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "trends and advancements in deep neural network communication")
  Member "speech commands a dataset for limited vocabulary speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "cmsis nn efficient neural network kernels for arm cortex m cpus":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "rnnpool efficient non linear pooling for ram constrained inference")
  Member "deep gradient compression reducing the communication bandwidth for distributed training":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "sparse weight activation training")
  Member "hello edge keyword spotting on microcontrollers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "small footprint open vocabulary keyword spotting with quantized lstm networks")
  Member "revisiting distributed synchronous sgd":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trends and advancements in deep neural network communication")
  Member "federated multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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)