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: "three mechanisms of weight decay regularization"
  Abstract: Weight decay is one of the standard tricks in the neural network toolbox, but the reasons for its regularization effect are poorly understood, and recent results have cast doubt on the traditional interpretation in terms of $L_2$ regularization. Literal weight decay has been shown to outperform $L_2$ regularization for optimizers for which they differ. We empirically investigate weight decay for three optimization algorithms (SGD, Adam, and K-FAC) and a variety of network architectures. We identify three distinct mechanisms by which weight decay exerts a regularization effect, depending on the particular optimization algorithm and architecture: (1) increasing the effective learning rate, (2) approximately regularizing the input-output Jacobian norm, and (3) reducing the effective damping coefficient for second-order optimization. Our results provide insight into how to improve the regularization of neural networks.

Connected through hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "linear context transform block"
    - "searching for mobilenetv3"
    - "a simple pooling based design for real time salient object detection"
    - "dabnet depth wise asymmetric bottleneck for real time semantic segmentation"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-2 (induced by source paper "rethinking the hyperparameters for fine tuning"):
  with members:
    - "task2vec task embedding for meta learning"
    - "rethinking imagenet pre training"
    - "large scale fine grained categorization and domain specific transfer learning"
    - "do better imagenet models transfer better"
    - "bag of tricks for image classification with convolutional neural networks"
    - "toward deeper understanding of nonconvex stochastic optimization with momentum using diffusion approximations"
    - "a disciplined approach to neural network hyper parameters part 1 learning rate batch size momentum and weight decay"
    - "domain adaptive transfer learning with specialist models"

Connected through hyperedge HE-3 (induced by source paper "learning cross domain generalizable features by representation disentanglement"):
  with members:
    - "a two step disentanglement method"
    - "domain agnostic learning with disentangled representations"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"
    - "domain generalization via model agnostic learning of semantic features"
    - "mixmatch a holistic approach to semi supervised learning"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"

Connected through hyperedge HE-4 (induced by source paper "fixmatch simplifying semi supervised learning with consistency and confidence"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "on empirical comparisons of optimizers for deep learning"
    - "pseudo labeling and confirmation bias in deep semi supervised learning"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "unsupervised data augmentation for consistency training"
    - "distribution density tails and outliers in machine learning metrics and applications"
    - "self training with noisy student improves imagenet classification"
    - "closing the generalization gap of adaptive gradient methods in training deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "topologically densified distributions"):
  with members:
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "neural persistence a complexity measure for deep neural networks using algebraic topology"
    - "a kernel theory of modern data augmentation"
    - "utilizing class information for deep network representation shaping"
    - "augment your batch better training with larger batches"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "a topological regularizer for classifiers via persistent homology"

Connected through hyperedge HE-6 (induced by source paper "the value of nullspace tuning using partial label information"):
  with members:
    - "mixmatch a holistic approach to semi supervised learning"
    - "coronary calcium detection using 3d attention identical dual deep network based on weakly supervised learning"
    - "inter scanner harmonization of high angular resolution dw mri using null space deep learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "mixup beyond empirical risk minimization"
    - "paying more attention to attention improving the performance of convolutional neural networks via attention transfer"

Connected through hyperedge HE-7 (induced by source paper "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "an empirical model of large batch training"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "eigenvalue corrected noisy natural gradient"

Connected through hyperedge HE-8 (induced by source paper "eigendamage structured pruning in the kronecker factored eigenbasis"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "eigenvalue corrected noisy natural gradient"
    - "rethinking the value of network pruning"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "learning efficient convolutional networks through network slimming"
    - "noisy natural gradient as variational inference"

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)