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: "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"
  Abstract: Over-parameterized deep neural networks trained by simple first-order methods are known to be able to fit any labeling of data. Such over-fitting ability hinders generalization when mislabeled training examples are present. On the other hand, simple regularization methods like early-stopping can often achieve highly nontrivial performance on clean test data in these scenarios, a phenomenon not theoretically understood. This paper proposes and analyzes two simple and intuitive regularization methods: (i) regularization by the distance between the network parameters to initialization, and (ii) adding a trainable auxiliary variable to the network output for each training example. Theoretically, we prove that gradient descent training with either of these two methods leads to a generalization guarantee on the clean data distribution despite being trained using noisy labels. Our generalization analysis relies on the connection between wide neural network and neural tangent kernel (NTK). The generalization bound is independent of the network size, and is comparable to the bound one can get when there is no label noise. Experimental results verify the effectiveness of these methods on noisily labeled datasets.

Connected through hyperedge HE-1 (induced by source paper "identifying mislabeled data using the area under the margin ranking"):
  with members:
    - "dividemix learning with noisy labels as semi supervised learning"
    - "convolutional networks with dense connectivity"
    - "towards robust learning with different label noise distributions"
    - "self learning to filter noisy labels with self ensembling"
    - "image classification with deep learning in the presence of noisy labels a survey"
    - "unsupervised label noise modeling and loss correction"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"

Connected through hyperedge HE-2 (induced by source paper "improving generalization by controlling label noise information in neural network weights"):
  with members:
    - "deep self learning from noisy labels"
    - "where is the information in a deep neural network"
    - "how does disagreement help generalization against label corruption"
    - "unsupervised label noise modeling and loss correction"
    - "meta learning without memorization"
    - "understanding and utilizing deep neural networks trained with noisy labels"
    - "meta weight net learning an explicit mapping for sample weighting"
    - "measuring the intrinsic dimension of objective landscapes"

Connected through hyperedge HE-3 (induced by source paper "taylorized training towards better approximation of neural network training at finite width"):
  with members:
    - "beyond linearization on quadratic and higher order approximation of wide neural networks"
    - "tensor programs i wide feedforward or recurrent neural networks of any architecture are gaussian processes"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "enhanced convolutional neural tangent kernels"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"

Connected through hyperedge HE-4 (induced by source paper "learning not to learn in the presence of noisy labels"):
  with members:
    - "laprop a better way to combine momentum with adaptive gradient"
    - "deep gamblers learning to abstain with portfolio theory"
    - "sgd on neural networks learns functions of increasing complexity"
    - "how does disagreement help generalization against label corruption"
    - "selectivenet a deep neural network with an integrated reject option"
    - "l_dmi an information theoretic noise robust loss function"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "beyond linearization on quadratic and higher order approximation of wide neural networks"):
  with members:
    - "kernel and deep regimes in overparametrized models"
    - "on nonconvex optimization for machine learning gradients stochasticity and saddle points"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-6 (induced by source paper "improving generalization and robustness with noisy collaboration in knowledge distillation"):
  with members:
    - "using videos to evaluate image model robustness"
    - "deep self learning from noisy labels"
    - "symmetric cross entropy for robust learning with noisy labels"
    - "certified adversarial robustness via randomized smoothing"
    - "when does label smoothing help"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "theoretically principled trade off between robustness and accuracy"

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)