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: "margins shrinkage and boosting"
  Abstract: This manuscript shows that AdaBoost and its immediate variants can produce approximate maximum margin classifiers simply by scaling step size choices with a fixed small constant. In this way, when the unscaled step size is an optimal choice, these results provide guarantees for Friedman's empirically successful "shrinkage" procedure for gradient boosting (Friedman, 2000). Guarantees are also provided for a variety of other step sizes, affirming the intuition that increasingly regularized line searches provide improved margin guarantees. The results hold for the exponential loss and similar losses, most notably the logistic loss.

Connected through hyperedge HE-1 (induced by source paper "gradient descent maximizes the margin of homogeneous neural networks"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "a refined primal dual analysis of the implicit bias"
    - "implicit regularization in deep matrix factorization"
    - "fixup initialization residual learning without normalization"
    - "on exact computation with an infinitely wide neural net"
    - "implicit regularization for deep neural networks driven by an ornstein uhlenbeck like process"
    - "sorting out lipschitz function approximation"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "inductive bias of gradient descent based adversarial training on separable data"):
  with members:
    - "convergence and margin of adversarial training on separable data"
    - "feature denoising for improving adversarial robustness"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "risk and parameter convergence of logistic regression"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "adversarial example defenses ensembles of weak defenses are not strong"

Connected through hyperedge HE-3 (induced by source paper "convergence and margin of adversarial training on separable data"):
  with members:
    - "adversarial examples are a natural consequence of test error in noise"
    - "does data augmentation lead to positive margin"
    - "benchmarking neural network robustness to common corruptions and surface variations"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial spheres"
    - "universal adversarial training"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-4 (induced by source paper "implicit bias of gradient descent on linear convolutional networks"):
  with members:
    - "risk and parameter convergence of logistic regression"
    - "geometry of optimization and implicit regularization in deep learning"
    - "don t decay the learning rate increase the batch size"
    - "sharp minima can generalize for deep nets"
    - "the loss surface of deep and wide neural networks"
    - "algorithmic regularization in over parameterized matrix recovery"
    - "learning to learn by gradient descent by gradient descent"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "on breiman s dilemma in neural networks phase transitions of margin dynamics"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "on convergence and stability of gans"
    - "a surprising linear relationship predicts test performance in deep networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "spectrally normalized margin bounds for neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "matching networks for one shot learning"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "risk and parameter convergence of logistic regression"):
  with members:
    - "coin betting and parameter free online learning"
    - "a primal dual convergence analysis of boosting"

Connected through hyperedge HE-7 (induced by source paper "when will gradient methods converge to max margin classifier under relu models"):
  with members:
    - "risk and parameter convergence of logistic regression"
    - "non strongly convex smooth stochastic approximation with convergence rate o 1 n"

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)