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: "globally optimal training of generalized polynomial neural networks with nonlinear spectral methods"
  Abstract: The optimization problem behind neural networks is highly non-convex. Training with stochastic gradient descent and variants requires careful parameter tuning and provides no guarantee to achieve the global optimum. In contrast we show under quite weak assumptions on the data that a particular class of feedforward neural networks can be trained globally optimal with a linear convergence rate with our nonlinear spectral method. Up to our knowledge this is the first practically feasible method which achieves such a guarantee. While the method can in principle be applied to deep networks, we restrict ourselves for simplicity in this paper to one and two hidden layer networks. Our experiments confirm that these models are rich enough to achieve good performance on a series of real-world datasets.

Connected through hyperedge HE-1 (induced by source paper "on connected sublevel sets in deep learning"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "understanding the loss surface of neural networks for binary classification"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"

Connected through hyperedge HE-2 (induced by source paper "when can neural networks learn connected decision regions"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "optimization landscape and expressivity of deep cnns"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "the loss surface of deep and wide neural networks"
    - "the expressive power of neural networks a view from the width"
    - "theory ii landscape of the empirical risk in deep learning"
    - "error bounds for approximations with deep relu networks"

Connected through hyperedge HE-3 (induced by source paper "on the benefit of width for neural networks disappearance of bad basins"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "roberta a robustly optimized bert pretraining approach"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"

Connected through hyperedge HE-4 (induced by source paper "over parameterized deep neural networks have no strict local minima for any continuous activations"):
  with members:
    - "autoslim towards one shot architecture search for channel numbers"
    - "roberta a robustly optimized bert pretraining approach"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "theoretical properties of the global optimizer of two layer neural network"
    - "on the power of over parametrization in neural networks with quadratic activation"

Connected through hyperedge HE-5 (induced by source paper "on the loss landscape of a class of deep neural networks with no bad local valleys"):
  with members:
    - "deep neural networks with multi branch architectures are less non convex"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "optimization landscape and expressivity of deep cnns"
    - "exploring generalization in deep learning"
    - "recovery guarantees for one hidden layer neural networks"
    - "failures of gradient based deep learning"
    - "depth creates no bad local minima"

Connected through hyperedge HE-6 (induced by source paper "understanding the loss surface of neural networks for binary classification"):
  with members:
    - "theoretical properties of the global optimizer of two layer neural network"
    - "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"
    - "recovery guarantees for one hidden layer neural networks"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the loss surface of deep and wide neural networks"
    - "global optimality conditions for deep neural networks"

Connected through hyperedge HE-7 (induced by source paper "a fully private pipeline for deep learning on electronic health records"):
  with members:
    - "the secret sharer measuring unintended neural network memorization extracting secrets"
    - "deep learning for metagenomic data using 2d embeddings and convolutional neural networks"
    - "a review of privacy and consent management in healthcare a focus on emerging data sources"
    - "identifying diabetic patients with high risk of readmission"
    - "faster arithmetic for number theoretic transforms"

Connected through hyperedge HE-8 (induced by source paper "when is a convolutional filter easy to learn"):
  with members:
    - "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"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "the loss surface of deep and wide neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"

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)