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: "quadratic suffices for over parametrization via matrix chernoff bound"
  Abstract: We improve the over-parametrization size over two beautiful results [Li and Liang' 2018] and [Du, Zhai, Poczos and Singh' 2019] in deep learning theory.

Connected through hyperedge HE-1 (induced by source paper "over parameterized adversarial training an analysis overcoming the curse of dimensionality"):
  with members:
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "introduction to online convex optimization"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural 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"
    - "on the convergence rate of training recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "learning parities with neural networks"):
  with members:
    - "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"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "neural networks learning and memorization with almost no over parameterization"

Connected through hyperedge HE-3 (induced by source paper "a corrective view of neural networks representation memorization and learning"):
  with members:
    - "effect of activation functions on the training of overparametrized neural nets"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "barron spaces and the compositional function spaces for neural network models"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "on the power and limitations of random features for understanding neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "a convergence theory for deep learning via over parameterization"

Connected through hyperedge HE-4 (induced by source paper "memory capacity of neural networks with threshold and relu activations"):
  with members:
    - "the capacity of feedforward neural networks"
    - "optimization for deep learning theory and algorithms"
    - "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"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "enhancing adversarial defense by k winners take all"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "hopskipjumpattack a query efficient decision based attack"
    - "theoretically principled trade off between robustness and accuracy"
    - "defensive quantization when efficiency meets robustness"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "adversarial training for free"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"

Connected through hyperedge HE-6 (induced by source paper "nearly minimal over parametrization of shallow neural networks"):
  with members:
    - "nonconvex optimization meets low rank matrix factorization an overview"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "trainability and data dependent initialization of over parameterized relu neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "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-7 (induced by source paper "dynamics of deep neural networks and neural tangent hierarchy"):
  with members:
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "elimination of all bad local minima in deep learning"
    - "on the power and limitations of random features for understanding neural networks"
    - "what can resnet learn efficiently going beyond kernels"
    - "learning and generalization in overparameterized neural networks going beyond two layers"

Connected through hyperedge HE-8 (induced by source paper "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"):
  with members:
    - "an improved analysis of training over parameterized deep 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"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "can sgd learn recurrent neural networks with provable generalization"
    - "what can resnet learn efficiently going beyond kernels"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu 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)