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 "rigging the lottery making all tickets winners"):
  Abstract: Sparse neural networks have been shown to be more parameter and compute efficient compared to dense networks and in some cases are used to decrease wall clock inference times. There is a large body of work on training dense networks to yield sparse networks for inference. This limits the size of the largest trainable sparse model to that of the largest trainable dense model. In this paper we introduce a method to train sparse neural networks with a fixed parameter count and a fixed computational cost throughout training, without sacrificing accuracy relative to existing dense-to-sparse training methods. Our method updates the topology of the network during training by using parameter magnitudes and infrequent gradient calculations. We show that this approach requires fewer floating-point operations (FLOPs) to achieve a given level of accuracy compared to prior techniques. Importantly, by adjusting the topology it can start from any initialization - not just "lucky" ones. We demonstrate state-of-the-art sparse training results with ResNet-50, MobileNet v1 and MobileNet v2 on the ImageNet-2012 dataset, WideResNets on the CIFAR-10 dataset and RNNs on the WikiText-103 dataset. Finally, we provide some insights into why allowing the topology to change during the optimization can overcome local minima encountered when the topology remains static.

Includes members:
  Member "deconstructing lottery tickets zeros signs and the supermask":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learned threshold pruning")
  Member "parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "the state of sparsity in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "soft threshold weight reparameterization for learnable sparsity")
  Member "mobilenetv2 inverted residuals and linear bottlenecks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "campfire compressible regularization free structured sparse training for hardware accelerators")
  Member "the lottery ticket hypothesis finding sparse trainable neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lossless compression of deep neural networks")
  Member "efficient neural audio synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "what is the state of neural network pruning")
  Member "snip single shot network pruning based on connection sensitivity":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discrimination aware network pruning for deep model compression")

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)