human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "adam induces implicit weight sparsity in rectifier neural networks"
  Abstract: In recent years, deep neural networks (DNNs) have been applied to various machine leaning tasks, including image recognition, speech recognition, and machine translation. However, large DNN models are needed to achieve state-of-the-art performance, exceeding the capabilities of edge devices. Model reduction is thus needed for practical use. In this paper, we point out that deep learning automatically induces group sparsity of weights, in which all weights connected to an output channel (node) are zero, when training DNNs under the following three conditions: (1) rectified-linear-unit (ReLU) activations, (2) an $L_2$-regularized objective function, and (3) the Adam optimizer. Next, we analyze this behavior both theoretically and experimentally, and propose a simple model reduction method: eliminate the zero weights after training the DNN. In experiments on MNIST and CIFAR-10 datasets, we demonstrate the sparsity with various training setups. Finally, we show that our method can efficiently reduce the model size and performs well relative to methods that use a sparsity-inducing regularizer.

Connected through hyperedge HE-1 (induced by source paper "exploiting the full capacity of deep neural networks while avoiding overfitting by targeted sparsity regularization"):
  with members:
    - "on correlation of features extracted by deep neural networks"
    - "on the convergence of adam and beyond"
    - "understanding the disharmony between dropout and batch normalization by variance shift"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "deep learning of constrained autoencoders for enhanced understanding of data"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mixup as locally linear out of manifold regularization"
    - "on implicit filter level sparsity in convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "how does bn increase collapsed neural network filters"):
  with members:
    - "towards understanding regularization in batch normalization"
    - "on implicit filter level sparsity in convolutional neural networks"
    - "dynamic runtime feature map pruning"
    - "focal loss for dense object detection"
    - "snapshot ensembles train 1 get m for free"
    - "exploring the regularity of sparse structure in convolutional neural networks"
    - "channel pruning for accelerating very deep neural networks"
    - "instance normalization the missing ingredient for fast stylization"

Connected through hyperedge HE-3 (induced by source paper "on implicit filter level sparsity in convolutional neural networks"):
  with members:
    - "on the convergence of adam and beyond"
    - "faster gaze prediction with dense networks and fisher pruning"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "revisiting the importance of individual units in cnns via ablation"
    - "rethinking the value of network pruning"
    - "learning efficient convolutional networks through network slimming"
    - "decoupled weight decay regularization"
    - "sharp minima can generalize for deep nets"

Question: Please tell me which class the center node 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)