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: "eigenvalue decay implies polynomial time learnability for neural networks"
  Abstract: We consider the problem of learning function classes computed by neural networks with various activations (e.g. ReLU or Sigmoid), a task believed to be computationally intractable in the worst-case. A major open problem is to understand the minimal assumptions under which these classes admit provably efficient algorithms. In this work we show that a natural distributional assumption corresponding to {\em eigenvalue decay} of the Gram matrix yields polynomial-time algorithms in the non-realizable setting for expressive classes of networks (e.g. feed-forward networks of ReLUs). We make no assumptions on the structure of the network or the labels. Given sufficiently-strong polynomial eigenvalue decay, we obtain {\em fully}-polynomial time algorithms in {\em all} the relevant parameters with respect to square-loss. Milder decay assumptions also lead to improved algorithms. This is the first purely distributional assumption that leads to polynomial-time algorithms for networks of ReLUs, even with one hidden layer. Further, unlike prior distributional assumptions (e.g., the marginal distribution is Gaussian), eigenvalue decay has been observed in practice on common data sets.

Connected through hyperedge HE-1 (induced by source paper "frequency bias in neural networks for input of non uniform density"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "towards understanding the spectral bias of deep learning"
    - "kernel and deep regimes in overparametrized models"
    - "frequency principle fourier analysis sheds light on deep 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 convergence rate of training recurrent neural networks"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning two layer networks with multinomial activation and high thresholds"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "recovery guarantees for one hidden layer neural networks"
    - "electron proton dynamics in deep learning"
    - "learning non overlapping convolutional neural networks with multiple kernels"
    - "learning neural networks with two nonlinear layers in polynomial time"

Connected through hyperedge HE-3 (induced by source paper "on the learnability of deep random networks"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "convergence results for neural networks via electrodynamics"
    - "learning non overlapping convolutional neural networks with multiple kernels"
    - "learning neural networks with two nonlinear layers in polynomial time"
    - "when is a convolutional filter easy to learn"
    - "learning depth three neural networks in polynomial time"

Connected through hyperedge HE-4 (induced by source paper "towards a theoretical understanding of hashing based neural nets"):
  with members:
    - "on the convergence rate of training recurrent neural networks"
    - "adversarial examples from cryptographic pseudo random generators"
    - "spectral signatures in backdoor attacks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "the computational complexity of training relu s"
    - "adversarially robust generalization requires more data"
    - "a convergence theory for deep learning via over parameterization"
    - "relative error tensor low rank approximation"

Connected through hyperedge HE-5 (induced by source paper "improved learning of one hidden layer convolutional neural networks with overlaps"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "on the power of over parametrization in neural networks with quadratic activation"
    - "learning one hidden layer neural networks with landscape design"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"

Connected through hyperedge HE-6 (induced by source paper "optimal sketching bounds for exp concave stochastic minimization"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "efficient second order online learning by sketching"
    - "solving ridge regression using sketched preconditioned svrg"
    - "fast rates with high probability in exp concave statistical learning"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-7 (induced by source paper "recovering the lowest layer of deep networks with high threshold activations"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "when is a convolutional filter easy to learn"
    - "learning depth three neural networks in polynomial time"
    - "reliably learning the relu in polynomial time"
    - "efficient approaches for escaping higher order saddle points in non convex optimization"
    - "provable bounds for learning some deep representations"

Connected through hyperedge HE-8 (induced by source paper "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"):
  with members:
    - "learning one hidden layer neural networks with landscape design"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "weight sharing is crucial to succesful optimization"
    - "recovery guarantees for one hidden layer neural networks"
    - "the loss surface of deep and wide 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)