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: "analysis of the gradient descent algorithm for a deep neural network model with skip connections"
  Abstract: The behavior of the gradient descent (GD) algorithm is analyzed for a deep neural network model with skip-connections. It is proved that in the over-parametrized regime, for a suitable initialization, with high probability GD can find a global minimum exponentially fast. Generalization error estimates along the GD path are also established. As a consequence, it is shown that when the target function is in the reproducing kernel Hilbert space (RKHS) with a kernel defined by the initialization, there exist generalizable early-stopping solutions along the GD path. In addition, it is also shown that the GD path is uniformly close to the functions given by the related random feature model. Consequently, in this "implicit regularization" setting, the deep neural network model deteriorates to a random feature model. Our results hold for neural networks of any width larger than the input dimension.

Connected through hyperedge HE-1 (induced by source paper "explicitizing an implicit bias of the frequency principle in two layer neural networks"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "a type of generalization error induced by initialization in deep neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "training behavior of deep neural network in frequency domain"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "frequency principle in deep learning with general loss functions and its potential application"

Connected through hyperedge HE-2 (induced by source paper "algorithm dependent generalization bounds for overparameterized deep residual 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"
    - "temporal convolution for real time keyword spotting on mobile devices"
    - "training over parameterized deep resnet is almost as easy as training a two layer network"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "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"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "over parameterized two level neural networks can learn near optimal feature representations"):
  with members:
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "can sgd learn recurrent neural networks with provable generalization"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "overparameterized nonlinear learning gradient descent takes the shortest path"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-4 (induced by source paper "a type of generalization error induced by initialization in deep neural networks"):
  with members:
    - "frequency principle fourier analysis sheds light on deep neural networks"
    - "theory iii dynamics and generalization in deep networks a simple solution"
    - "meta learners learning dynamics are unlike learners"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a comparative analysis of the optimization and generalization property of two layer neural network and random feature models under gradient descent dynamics"
    - "the impact of neural network overparameterization on gradient confusion and stochastic gradient descent"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "training behavior of deep neural network in frequency domain"

Connected through hyperedge HE-5 (induced by source paper "global convergence of gradient descent for deep linear residual networks"):
  with members:
    - "width provably matters in optimization for deep linear neural networks"
    - "fixup initialization residual learning without normalization"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "stochastic gradient descent optimizes over parameterized deep relu networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "a convergence theory for deep learning via over parameterization"

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)