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: "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
  Abstract: A fairly comprehensive analysis is presented for the gradient descent dynamics for training two-layer neural network models in the situation when the parameters in both layers are updated. General initialization schemes as well as general regimes for the network width and training data size are considered. In the over-parametrized regime, it is shown that gradient descent dynamics can achieve zero training loss exponentially fast regardless of the quality of the labels. In addition, it is proved that throughout the training process the functions represented by the neural network model are uniformly close to that of a kernel method. For general values of the network width and training data size, sharp estimates of the generalization error is established for target functions in the appropriate reproducing kernel Hilbert space. Our analysis suggests strongly that in terms of `implicit regularization', two-layer neural network models do not outperform the kernel method.

Connected through hyperedge HE-1 (induced by source paper "learning parities with neural networks"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "mildly overparametrized neural nets can memorize training data efficiently"
    - "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"
    - "neural networks learning and memorization with almost no over parameterization"

Connected through hyperedge HE-2 (induced by source paper "a theoretical analysis of deep q learning"):
  with members:
    - "a theory of regularized markov decision processes"
    - "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"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "actor critic policy optimization in partially observable multiagent environments"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-3 (induced by source paper "the local elasticity of neural networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "implicit regularization in deep matrix factorization"
    - "efficient approximation of deep relu networks for functions on low dimensional manifolds"
    - "stiffness a new perspective on generalization in neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "what can resnet learn efficiently going beyond kernels"

Connected through hyperedge HE-4 (induced by source paper "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"):
  with members:
    - "two models of double descent for weak features"
    - "size free generalization bounds for convolutional neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "training over parameterized deep resnet is almost as easy as training a two layer network"
    - "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"
    - "on the power and limitations of random features for understanding neural networks"

Connected through hyperedge HE-5 (induced by source paper "generalization bounds of stochastic gradient descent for wide and deep neural networks"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "size free generalization bounds for convolutional neural networks"
    - "generalization error bounds of gradient descent for learning over parameterized deep relu networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow 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"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"

Connected through hyperedge HE-6 (induced by source paper "learning boolean circuits with neural networks"):
  with members:
    - "decoupled greedy learning of cnns"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "id3 learns juntas for smoothed product distributions"
    - "on the learnability of deep random networks"
    - "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"
    - "is deeper better only when shallow is good"

Connected through hyperedge HE-7 (induced by source paper "explicitizing an implicit bias of the frequency principle in two layer neural networks"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "a type of generalization error induced by initialization in deep neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "training behavior of deep neural network in frequency domain"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "frequency principle in deep learning with general loss functions and its potential application"

Connected through hyperedge HE-8 (induced by source paper "over parameterized two level neural networks can learn near optimal feature representations"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "can sgd learn recurrent neural networks with provable generalization"
    - "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in 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)