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: "on the implicit bias of dropout"
  Abstract: Algorithmic approaches endow deep learning systems with implicit bias that helps them generalize even in over-parametrized settings. In this paper, we focus on understanding such a bias induced in learning through dropout, a popular technique to avoid overfitting in deep learning. For single hidden-layer linear neural networks, we show that dropout tends to make the norm of incoming/outgoing weight vectors of all the hidden nodes equal. In addition, we provide a complete characterization of the optimization landscape induced by dropout.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

Connected through hyperedge HE-2 (induced by source paper "dropout explicit forms and capacity control"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "brain tumor segmentation with deep neural networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "size independent sample complexity of neural networks"
    - "algorithmic regularization in over parameterized matrix sensing and neural networks with quadratic activations"

Connected through hyperedge HE-3 (induced by source paper "on dropout and nuclear norm regularization"):
  with members:
    - "stochastic gradient mirror descent minimax optimality and implicit regularization"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the relationship between dropout and equiangular tight frames"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "a comprehensive analysis of deep regression"
    - "deep linear neural networks with arbitrary loss all local minima are global"

Connected through hyperedge HE-4 (induced by source paper "robust deep networks with randomized tensor regression layers"):
  with members:
    - "randomized tensor ring decomposition and its application to large scale data reconstruction"
    - "regularization for deep learning a taxonomy"
    - "a practical randomized cp tensor decomposition"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "tensor regression networks"
    - "tensor regression networks with various low rank tensor approximations"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"
    - "tensor contraction layers for parsimonious deep nets"

Connected through hyperedge HE-5 (induced by source paper "deep neural networks abstract like humans"):
  with members:
    - "exploring generalization in deep learning"
    - "visualizing the loss landscape of neural nets"
    - "the loss surface of deep and wide neural networks"
    - "opening the black box of deep neural networks via information"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a downsampled variant of imagenet as an alternative to the cifar datasets"
    - "understanding deep learning requires rethinking generalization"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"

Connected through hyperedge HE-6 (induced by source paper "on the regularization properties of structured dropout"):
  with members:
    - "dropblock a regularization method for convolutional networks"
    - "shakedrop regularization for deep residual learning"
    - "shake shake regularization"
    - "dropout as a low rank regularizer for matrix factorization"

Connected through hyperedge HE-7 (induced by source paper "loss landscapes of regularized linear autoencoders"):
  with members:
    - "deep learning without weight transport"
    - "assessing the scalability of biologically motivated deep learning algorithms and architectures"
    - "global optimality in low rank matrix optimization"
    - "the loss surfaces of multilayer networks"

Connected through hyperedge HE-8 (induced by source paper "on the relationship between dropout and equiangular tight frames"):
  with members:
    - "regularization for deep learning a taxonomy"
    - "deep metric learning with hierarchical triplet loss"
    - "dropout is a special case of the stochastic delta rule faster and more accurate deep learning"
    - "frame moments and welch bound with erasures"
    - "focal loss for dense object detection"
    - "learning deep networks from noisy labels with dropout regularization"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "random subsets of structured deterministic frames have manova spectra"

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)