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 additional mechanisms on clients to reduce communication costs"):
  Abstract: Federated learning (FL) enables on-device training over distributed networks consisting of a massive amount of modern smart devices, such as smartphones and IoT (Internet of Things) devices. However, the leading optimization algorithm in such settings, i.e., federated averaging (FedAvg), suffers from heavy communication costs and the inevitable performance drop, especially when the local data is distributed in a non-IID way. To alleviate this problem, we propose two potential solutions by introducing additional mechanisms to the on-device training. #R##N#The first (FedMMD) is adopting a two-stream model with the MMD (Maximum Mean Discrepancy) constraint instead of a single model in vanilla FedAvg to be trained on devices. Experiments show that the proposed method outperforms baselines, especially in non-IID FL settings, with a reduction of more than 20% in required communication rounds. #R##N#The second is FL with feature fusion (FedFusion). By aggregating the features from both the local and global models, we achieve higher accuracy at fewer communication costs. Furthermore, the feature fusion modules offer better initialization for newly incoming clients and thus speed up the process of convergence. Experiments in popular FL scenarios show that our FedFusion outperforms baselines in both accuracy and generalization ability while reducing the number of required communication rounds by more than 60%.

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 "leaf a benchmark for federated settings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "think locally act globally federated learning with local and global representations")
  Member "communication efficient on device machine learning federated distillation and augmentation under non iid private data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster on device training using new federated momentum algorithm")
  Member "federated meta learning for recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi step model agnostic meta learning convergence and improved algorithms")
  Member "federated learning with non iid data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "expanding the reach of federated learning by reducing client resource requirements":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "trends and advancements in deep neural network communication")
  Member "federated meta learning with fast convergence and efficient communication":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "survey of personalization techniques for federated learning")
  Member "continual learning through synaptic intelligence":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "incremental object detection via meta learning")

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)