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: "gradient dynamics of shallow univariate relu networks"
  Abstract: We present a theoretical and empirical study of the gradient dynamics of overparameterized shallow ReLU networks with one-dimensional input, solving least-squares interpolation. We show that the gradient dynamics of such networks are determined by the gradient flow in a non-redundant parameterization of the network function. We examine the principal qualitative features of this gradient flow. In particular, we determine conditions for two learning regimes:kernel and adaptive, which depend both on the relative magnitude of initialization of weights in different layers and the asymptotic behavior of initialization coefficients in the limit of large network widths. We show that learning in the kernel regime yields smooth interpolants, minimizing curvature, and reduces to cubic splines for uniform initializations. Learning in the adaptive regime favors instead linear splines, where knots cluster adaptively at the sample points.

Connected through hyperedge HE-1 (induced by source paper "convex geometry and duality of over parameterized neural networks"):
  with members:
    - "how do infinite width bounded norm networks look in function space"
    - "on exact computation with an infinitely wide neural net"
    - "implicit regularization for deep neural networks driven by an ornstein uhlenbeck like process"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-2 (induced by source paper "neural spectrum alignment"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "generalization guarantees for neural networks via harnessing the low rank structure of the jacobian"
    - "kernel and deep regimes in overparametrized models"
    - "asymptotics of wide networks from feynman diagrams"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "general probabilistic surface optimization and log density estimation"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-3 (induced by source paper "the implicit bias of depth how incremental learning drives generalization"):
  with members:
    - "implicit regularization of discrete gradient dynamics in linear neural networks"
    - "sgd on neural networks learns functions of increasing complexity"
    - "kernel and deep regimes in overparametrized models"
    - "the convergence rate of neural networks for learned functions of different frequencies"
    - "implicit regularization in deep matrix factorization"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "on the learning dynamics of deep 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)