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: "generalization in deep networks the role of distance from initialization"
  Abstract: Why does training deep neural networks using stochastic gradient descent (SGD) result in a generalization error that does not worsen with the number of parameters in the network? To answer this question, we advocate a notion of effective model capacity that is dependent on {\em a given random initialization of the network} and not just the training algorithm and the data distribution. We provide empirical evidences that demonstrate that the model capacity of SGD-trained deep networks is in fact restricted through implicit regularization of {\em the $\ell_2$ distance from the initialization}. We also provide theoretical arguments that further highlight the need for initialization-dependent notions of model capacity. We leave as open questions how and why distance from initialization is regularized, and whether it is sufficient to explain generalization.

Connected through hyperedge HE-1 (induced by source paper "observational overfitting in reinforcement learning"):
  with members:
    - "generalization in reinforcement learning with selective noise injection and information bottleneck"
    - "on the generalization gap in reparameterizable reinforcement learning"
    - "weight agnostic neural networks"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "the gap between model based and model free methods on the linear quadratic regulator an asymptotic viewpoint"
    - "simple random search provides a competitive approach to reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"):
  with members:
    - "a mean field theory of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "fixup initialization residual learning without normalization"
    - "stiffness a new perspective on generalization in neural networks"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"

Connected through hyperedge HE-3 (induced by source paper "fantastic generalization measures and where to find them"):
  with members:
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-4 (induced by source paper "on the difficulty of warm starting neural network training"):
  with members:
    - "an analysis of pre training on object detection"
    - "deep batch active learning by diverse uncertain gradient lower bounds"
    - "energy and policy considerations for deep learning in nlp"
    - "green ai"
    - "on first order meta learning algorithms"
    - "reptile a scalable metalearning algorithm"
    - "model based active exploration"
    - "fisher rao metric geometry and complexity of neural networks"

Connected through hyperedge HE-5 (induced by source paper "simple and effective regularization methods for training on noisily labeled data with generalization guarantee"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "how does disagreement help generalization against label corruption"
    - "on exact computation with an infinitely wide neural net"
    - "gradient descent with early stopping is provably robust to label noise for overparameterized neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "deep learning is robust to massive label noise"

Connected through hyperedge HE-6 (induced by source paper "the generalization stability tradeoff in neural network pruning"):
  with members:
    - "gate decorator global filter pruning method for accelerating deep convolutional neural networks"
    - "sparse networks from scratch faster training without losing performance"
    - "the state of sparsity in deep neural networks"
    - "rethinking the smaller norm less informative assumption in channel pruning of convolution layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "learning efficient convolutional networks through network slimming"

Connected through hyperedge HE-7 (induced by source paper "generalization bounds for deep convolutional neural networks"):
  with members:
    - "norm based generalisation bounds for multi class convolutional neural networks"
    - "fantastic generalization measures and where to find them"
    - "generalization bounds for neural networks via approximate description length"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "the singular values of convolutional layers"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-8 (induced by source paper "the intriguing role of module criticality in the generalization of deep networks"):
  with members:
    - "similarity of neural network representations revisited"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "generalization bounds for deep convolutional neural networks"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "the singular values of convolutional layers"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"

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)