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: "asymptotics of wide networks from feynman diagrams"
  Abstract: Understanding the asymptotic behavior of wide networks is of considerable interest. In this work, we present a general method for analyzing this large width behavior. The method is an adaptation of Feynman diagrams, a standard tool for computing multivariate Gaussian integrals. We apply our method to study training dynamics, improving existing bounds and deriving new results on wide network evolution during stochastic gradient descent. Going beyond the strict large width limit, we present closed-form expressions for higher-order terms governing wide network training, and test these predictions empirically.

Connected through hyperedge HE-1 (induced by source paper "on the infinite width limit of neural networks with a standard parameterization"):
  with members:
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "harnessing the power of infinitely wide deep nets on small data tasks"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "on exact computation with an infinitely wide neural net"
    - "information in infinite ensembles of infinitely wide neural networks"
    - "graph neural tangent kernel fusing graph neural networks with graph kernels"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-2 (induced by source paper "disentangling feature and lazy learning in deep neural networks an empirical study"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "the asymptotic spectrum of the hessian of dnn throughout training"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a modern take on the bias variance tradeoff in neural networks"

Connected through hyperedge HE-3 (induced by source paper "disentangling feature and lazy training in deep neural networks"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "the effect of network width on stochastic gradient descent and generalization an empirical study"
    - "the asymptotic spectrum of the hessian of dnn throughout training"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "a modern take on the bias variance tradeoff in neural networks"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-4 (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"
    - "gradient dynamics of shallow univariate relu networks"
    - "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-5 (induced by source paper "finite depth and width corrections to the neural tangent kernel"):
  with members:
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "two models of double descent for weak features"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "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)