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: "ell_1 regularized neural networks are improperly learnable in polynomial time"
  Abstract: We study the improper learning of multi-layer neural networks. Suppose that the neural network to be learned has $k$ hidden layers and that the $\ell_1$-norm of the incoming weights of any neuron is bounded by $L$. We present a kernel-based method, such that with probability at least $1 - \delta$, it learns a predictor whose generalization error is at most $\epsilon$ worse than that of the neural network. The sample complexity and the time complexity of the presented method are polynomial in the input dimension and in $(1/\epsilon,\log(1/\delta),F(k,L))$, where $F(k,L)$ is a function depending on $(k,L)$ and on the activation function, independent of the number of neurons. The algorithm applies to both sigmoid-like activation functions and ReLU-like activation functions. It implies that any sufficiently sparse neural network is learnable in polynomial time.

Connected through hyperedge HE-1 (induced by source paper "generalised lipschitz regularisation equals distributional robustness"):
  with members:
    - "naive feature selection sparsity in naive bayes"
    - "sorting out lipschitz function approximation"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "spectral normalization for generative adversarial networks"
    - "generalizable adversarial training via spectral normalization"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "universal adversarial perturbations"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-2 (induced by source paper "what can resnet learn efficiently going beyond kernels"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "can sgd learn recurrent neural networks with provable generalization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning two layer rectified neural networks in polynomial time"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-3 (induced by source paper "knowledge representing efficient sparse representation of prior knowledge for knowledge distillation"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge distillation with adversarial samples supporting decision boundary"
    - "learning strict identity mappings in deep residual networks"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "data driven sparse structure selection for deep neural networks"
    - "a geometric view of optimal transportation and generative model"
    - "thinet a filter level pruning method for deep neural network compression"
    - "like what you like knowledge distill via neuron selectivity transfer"

Connected through hyperedge HE-4 (induced by source paper "on the power and limitations of random features for understanding neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "why do larger models generalize better a theoretical perspective via the xor problem"
    - "a convergence theory for deep learning via over parameterization"
    - "on the power of over parametrization in neural networks with quadratic activation"

Connected through hyperedge HE-5 (induced by source paper "robust deep networks with randomized tensor regression layers"):
  with members:
    - "randomized tensor ring decomposition and its application to large scale data reconstruction"
    - "regularization for deep learning a taxonomy"
    - "on the implicit bias of dropout"
    - "a practical randomized cp tensor decomposition"
    - "improving dnn robustness to adversarial attacks using jacobian regularization"
    - "tensor regression networks"
    - "tensor regression networks with various low rank tensor approximations"
    - "foolbox v0 8 0 a python toolbox to benchmark the robustness of machine learning models"

Connected through hyperedge HE-6 (induced by source paper "optimizing for interpretability in deep neural networks with tree regularization"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "methods for interpreting and understanding deep neural networks"
    - "explanation in artificial intelligence insights from the social sciences"
    - "right for the right reasons training differentiable models by constraining their explanations"
    - "distilling a neural network into a soft decision tree"
    - "an unexpected unity among methods for interpreting model predictions"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "harnessing deep neural networks with logic rules"

Connected through hyperedge HE-7 (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-8 (induced by source paper "renyi differentially private admm based l1 regularized classification"):
  with members:
    - "concentrated differentially private gradient descent with adaptive per iteration privacy budget"
    - "improving the privacy and accuracy of admm based distributed algorithms"
    - "subsampled r enyi differential privacy and analytical moments accountant"
    - "efficient private erm for smooth objectives"
    - "renyi differential privacy"
    - "bolt on differential privacy for scalable stochastic gradient descent based analytics"
    - "calibrating data to sensitivity in private data analysis"
    - "differentially private empirical risk minimization"

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)