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 "identifying critical neurons in ann architectures using mixed integer programming"):
  Abstract: We introduce a novel approach to optimize the architecture of deep neural networks by identifying critical neurons and removing non-critical ones. The proposed approach utilizes a mixed integer programming (MIP) formulation of neural models which includes a continuous importance score computed for each neuron in the network. The optimization in MIP solver minimizes the number of critical neurons (i.e., with high importance score) that need to be kept for maintaining the overall accuracy of the model. Further, the proposed formulation generalizes the recently considered lottery ticket optimization by identifying multiple "lucky" sub-networks resulting in optimized architecture that not only perform well on a single dataset, but also generalize across multiple ones upon retraining of network weights. Finally, the proposed framework provides significant improvement in scalability of automatic sparsification of deep network architectures compared to previous attempts. We validate the performance and generalizability of our approach on MNIST, Fashion-MNIST, and CIFAR-10 datasets, using three different neural networks: LeNet 5 and two ReLU fully connected models.

Includes members:
  Member "lossless compression of deep neural networks":
    also connected through other hyperedges:
      (none)
  Member "eigendamage structured pruning in the kronecker factored eigenbasis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "picking winning tickets before training by preserving gradient flow")
  Member "pruning at a glance global neural pruning for model compression":
    also connected through other hyperedges:
      (none)
  Member "a benchmark for interpretability methods in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overinterpretation reveals image classification model pathologies")
  Member "pytorch an imperative style high performance deep learning library":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "understanding individual neuron importance using information theory":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on iterative neural network pruning reinitialization and the similarity of masks")
  Member "pruning deep neural networks using partial least squares":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "covariance free partial least squares an incremental dimensionality reduction method")
  Member "towards understanding the role of over parametrization in generalization of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the implicit and explicit regularization effects of dropout")

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)