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 "adaptive gradient sparsification for efficient federated learning an online learning approach"):
  Abstract: Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation and synchronization steps. To reduce the communication overhead and improve the overall efficiency of FL, gradient sparsification (GS) can be applied, where instead of the full gradient, only a small subset of important elements of the gradient is communicated. Existing work on GS uses a fixed degree of gradient sparsity for i.i.d.-distributed data within a datacenter. In this paper, we consider adaptive degree of sparsity and non-i.i.d. local datasets. We first present a fairness-aware GS method which ensures that different clients provide a similar amount of updates. Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation trade-off that is controlled by the degree of gradient sparsity. The online learning algorithm uses an estimated sign of the derivative of the objective function, which gives a regret bound that is asymptotically equal to the case where exact derivative is available. Experiments with real datasets confirm the benefits of our proposed approaches, showing up to $40\%$ improvement in model accuracy for a finite training time.

Includes members:
  Member "model pruning enables efficient federated learning on edge devices":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "federated learning for resource constrained iot devices panoramas and state of the art")
  Member "a distributed synchronous sgd algorithm with global top k sparsification for low bandwidth networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "mdldroid a chainsgd reduce approach to mobile deep learning for personal mobile sensing")
  Member "advances and open problems in federated learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trends and advancements in deep neural network communication")
  Member "introduction to online convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beyond ucb optimal and efficient contextual bandits with regression oracles")
  Member "federated learning challenges methods and future directions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "trends and advancements in deep neural network communication")
  Member "layer wise adaptive gradient sparsification for distributed deep learning with convergence guarantees":
    also connected through other hyperedges:
      (none)
  Member "federated machine learning concept and applications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the future of digital health with federated learning")
  Member "wireless network intelligence at the edge":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "convergence time optimization for federated learning over wireless networks")

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)