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 "on iterative neural network pruning reinitialization and the similarity of masks"):
  Abstract: We examine how recently documented, fundamental phenomena in deep learning models subject to pruning are affected by changes in the pruning procedure. Specifically, we analyze differences in the connectivity structure and learning dynamics of pruned models found through a set of common iterative pruning techniques, to address questions of uniqueness of trainable, high-sparsity sub-networks, and their dependence on the chosen pruning method. In convolutional layers, we document the emergence of structure induced by magnitude-based unstructured pruning in conjunction with weight rewinding that resembles the effects of structured pruning. We also show empirical evidence that weight stability can be automatically achieved through apposite pruning techniques.

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 "on activation function coresets for network pruning":
    also connected through other hyperedges:
      (none)
  Member "exploring randomly wired neural networks for image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "automl zero evolving machine learning algorithms from scratch")
  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 "how important is a neuron":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards understanding neural machine translation with word importance")
  Member "understanding individual neuron importance using information theory":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identifying critical neurons in ann architectures using mixed integer programming")
  Member "learning and generalization in overparameterized neural networks going beyond two layers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "backward feature correction how deep learning performs deep learning")
  Member "revisiting the importance of individual units in cnns via ablation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hrank filter pruning using high rank feature map")

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)