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: "theory of deep learning iii explaining the non overfitting puzzle"
  Abstract: A main puzzle of deep networks revolves around the absence of overfitting despite large overparametrization and despite the large capacity demonstrated by zero training error on randomly labeled data. In this note, we show that the dynamics associated to gradient descent minimization of nonlinear networks is topologically equivalent, near the asymptotically stable minima of the empirical error, to linear gradient system in a quadratic potential with a degenerate (for square loss) or almost degenerate (for logistic or crossentropy loss) Hessian. The proposition depends on the qualitative theory of dynamical systems and is supported by numerical results. Our main propositions extend to deep nonlinear networks two properties of gradient descent for linear networks, that have been recently established (1) to be key to their generalization properties: 1. Gradient descent enforces a form of implicit regularization controlled by the number of iterations, and asymptotically converges to the minimum norm solution for appropriate initial conditions of gradient descent. This implies that there is usually an optimum early stopping that avoids overfitting of the loss. This property, valid for the square loss and many other loss functions, is relevant especially for regression. 2. For classification, the asymptotic convergence to the minimum norm solution implies convergence to the maximum margin solution which guarantees good classification error for "low noise" datasets. This property holds for loss functions such as the logistic and cross-entropy loss independently of the initial conditions. The robustness to overparametrization has suggestive implications for the robustness of the architecture of deep convolutional networks with respect to the curse of dimensionality.

Connected through hyperedge HE-1 (induced by source paper "removable and or repeated units emerge in overparametrized deep neural networks"):
  with members:
    - "kernel and deep regimes in overparametrized models"
    - "intrinsic dimension of data representations in deep neural networks"
    - "cop customized deep model compression via regularized correlation based filter level pruning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-2 (induced by source paper "deep gamblers learning to abstain with portfolio theory"):
  with members:
    - "one shot federated learning"
    - "selectivenet a deep neural network with an integrated reject option"
    - "on the convergence of adam and beyond"
    - "multimodal language analysis with recurrent multistage fusion"
    - "deep learning is robust to massive label noise"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "attention is all you need"
    - "neural machine translation and sequence to sequence models a tutorial"

Connected through hyperedge HE-3 (induced by source paper "minibatch processing in spiking neural networks"):
  with members:
    - "improved robustness of reinforcement learning policies upon conversion to spiking neuronal network platforms applied to atari games"
    - "deep learning in spiking neural networks"
    - "temporal coding in spiking neural networks with alpha synaptic function"
    - "surrogate gradient learning in spiking neural networks"
    - "nengodl combining deep learning and neuromorphic modelling methods"
    - "going deeper in spiking neural networks vgg and residual architectures"
    - "bindsnet a machine learning oriented spiking neural networks library in python"
    - "revisiting small batch training for deep neural networks"

Connected through hyperedge HE-4 (induced by source paper "bad global minima exist and sgd can reach them"):
  with members:
    - "cinic 10 is not imagenet or cifar 10"
    - "exploring generalization in deep learning"
    - "implicit regularization in deep learning"
    - "geometry of optimization and implicit regularization in deep learning"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-5 (induced by source paper "layer dynamics of linearised neural nets"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "theory iiib generalization in deep networks"
    - "lstm a search space odyssey"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "theory ii landscape of the empirical risk in deep learning"
    - "identity matters in deep learning"
    - "dynamics of stochastic gradient algorithms"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-6 (induced by source paper "gradient based neural dag learning"):
  with members:
    - "dag gnn dag structure learning with graph neural networks"
    - "domain adaptation by using causal inference to predict invariant conditional distributions"
    - "made masked autoencoder for distribution estimation"
    - "bayesian network learning with cutting planes"

Connected through hyperedge HE-7 (induced by source paper "generalization error in deep learning"):
  with members:
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "dnn or k nn that is the generalize vs memorize question"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "adversarially robust generalization requires more data"
    - "deep neural networks with multi branch architectures are less non convex"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "threat of adversarial attacks on deep learning in computer vision a survey"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-8 (induced by source paper "dnn or k nn that is the generalize vs memorize question"):
  with members:
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "exploring generalization in deep learning"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "significance of softmax based features in comparison to distance metric learning based features"
    - "sharp minima can generalize for deep nets"
    - "towards end to end speech recognition with deep convolutional neural networks"

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)