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 "model fusion via optimal transport"):
  Abstract: Combining different models is a widely used paradigm in machine learning applications. While the most common approach is to form an ensemble of models and average their individual predictions, this approach is often rendered infeasible by given resource constraints in terms of memory and computation, which grow linearly with the number of models. We present a layer-wise model fusion algorithm for neural networks that utilizes optimal transport to (soft-) align neurons across the models before averaging their associated parameters. We discuss two main strategies for fusing neural networks in this "one-shot" manner, without requiring any retraining. Next, we illustrate how this significantly outperforms vanilla averaging on convolutional networks (like VGG11), residual networks (like ResNet18), and multi-layer perceptrons, on CIFAR10 and MNIST. Finally, we show applications to transfer tasks (where our fused model even surpasses the performance of both the original models) as well as for compressing models. Code will be made available under the following link https://github.com/modelfusion.

Includes members:
  Member "federated learning with matched averaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey towards federated semi supervised learning")
  Member "wasserstein barycenter model ensembling":
    also connected through other hyperedges:
      (none)
  Member "how important is a neuron":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on iterative neural network pruning reinitialization and the similarity of masks")
  Member "non iterative knowledge fusion in deep convolutional neural networks":
    also connected through other hyperedges:
      (none)
  Member "context mover s distance barycenters optimal transport of contexts for building representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "structural aware sentence similarity with recursive optimal transport")
  Member "near linear time approximation algorithms for optimal transport via sinkhorn iteration":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric dataset distances via optimal transport")
  Member "axiomatic attribution for deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial attacks and defenses on graphs a review and empirical study")
  Member "communication efficient learning of deep networks from decentralized data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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)