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: "understanding generalization and optimization performance of deep cnns"
  Abstract: This work aims to provide understandings on the remarkable success of deep convolutional neural networks (CNNs) by theoretically analyzing their generalization performance and establishing optimization guarantees for gradient descent based training algorithms. Specifically, for a CNN model consisting of $l$ convolutional layers and one fully connected layer, we prove that its generalization error is bounded by $\mathcal{O}(\sqrt{\dt\widetilde{\varrho}/n})$ where $\theta$ denotes freedom degree of the network parameters and $\widetilde{\varrho}=\mathcal{O}(\log(\prod_{i=1}^{l}\rwi{i} (\ki{i}-\si{i}+1)/p)+\log(\rf))$ encapsulates architecture parameters including the kernel size $\ki{i}$, stride $\si{i}$, pooling size $p$ and parameter magnitude $\rwi{i}$. To our best knowledge, this is the first generalization bound that only depends on $\mathcal{O}(\log(\prod_{i=1}^{l+1}\rwi{i}))$, tighter than existing ones that all involve an exponential term like $\mathcal{O}(\prod_{i=1}^{l+1}\rwi{i})$. Besides, we prove that for an arbitrary gradient descent algorithm, the computed approximate stationary point by minimizing empirical risk is also an approximate stationary point to the population risk. This well explains why gradient descent training algorithms usually perform sufficiently well in practice. Furthermore, we prove the one-to-one correspondence and convergence guarantees for the non-degenerate stationary points between the empirical and population risks. It implies that the computed local minimum for the empirical risk is also close to a local minimum for the population risk, thus ensuring the good generalization performance of CNNs.

Connected through hyperedge HE-1 (induced by source paper "the role of invariance in spectral complexity based generalization bounds"):
  with members:
    - "size free generalization bounds for convolutional neural networks"
    - "uniform convergence may be unable to explain generalization in deep learning"
    - "information matrices and generalization"
    - "data dependent sample complexity of deep neural networks via lipschitz augmentation"
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "exploring generalization in deep learning"

Connected through hyperedge HE-2 (induced by source paper "graph neural networks exponentially lose expressive power for node classification"):
  with members:
    - "optuna a next generation hyperparameter optimization framework"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "on the universality of invariant networks"
    - "chainer a deep learning framework for accelerating the research cycle"
    - "simplifying graph convolutional networks"
    - "stability and generalization of graph convolutional neural networks"
    - "a comprehensive survey on graph neural networks"
    - "deepgcns can gcns go as deep as cnns"

Connected through hyperedge HE-3 (induced by source paper "fine grained optimization of deep neural networks"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "exploring generalization in deep learning"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "squeeze and excitation networks"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"

Connected through hyperedge HE-4 (induced by source paper "generalization bounds for deep convolutional neural networks"):
  with members:
    - "norm based generalisation bounds for multi class convolutional neural networks"
    - "generalization in deep networks the role of distance from initialization"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "size free generalization bounds for convolutional neural networks"):
  with members:
    - "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"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-6 (induced by source paper "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "traditional and heavy tailed self regularization in neural network models"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "theory iiib generalization in deep networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "a surprising linear relationship predicts test performance in deep networks"

Connected through hyperedge HE-7 (induced by source paper "some limitations of norm based generalization bounds in deep neural networks"):
  with members:
    - "identifying generalization properties in neural networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "computing nonvacuous generalization bounds for deep stochastic neural networks with many more parameters than training data"
    - "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"
    - "understanding deep learning requires rethinking generalization"

Connected through hyperedge HE-8 (induced by source paper "investigating convolutional neural networks using spatial orderness"):
  with members:
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "measuring the tendency of cnns to learn surface statistical regularities"
    - "deep vs shallow networks an approximation theory perspective"
    - "deep residual learning for image recognition"
    - "on the depth of deep neural networks a theoretical view"
    - "very deep convolutional networks for large scale image recognition"
    - "visualizing and understanding convolutional 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)