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: "algorithm dependent generalization bounds for overparameterized deep residual networks"
  Abstract: The skip-connections used in residual networks have become a standard architecture choice in deep learning due to the increased training stability and generalization performance with this architecture, although there has been limited theoretical understanding for this improvement. In this work, we analyze overparameterized deep residual networks trained by gradient descent following random initialization, and demonstrate that (i) the class of networks learned by gradient descent constitutes a small subset of the entire neural network function class, and (ii) this subclass of networks is sufficiently large to guarantee small training error. By showing (i) we are able to demonstrate that deep residual networks trained with gradient descent have a small generalization gap between training and test error, and together with (ii) this guarantees that the test error will be small. Our optimization and generalization guarantees require overparameterization that is only logarithmic in the depth of the network, while all known generalization bounds for deep non-residual networks have overparameterization requirements that are at least polynomial in the depth. This provides an explanation for why residual networks are preferable to non-residual ones.

Connected through hyperedge HE-1 (induced by source paper "on the global convergence of training deep linear resnets"):
  with members:
    - "provable benefit of orthogonal initialization in optimizing deep linear networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "width provably matters in optimization for deep linear neural networks"
    - "on learning over parameterized neural networks a functional approximation prospective"
    - "training over parameterized deep resnet is almost as easy as training a two layer network"
    - "on exact computation with an infinitely wide neural net"
    - "global convergence of gradient descent for deep linear residual networks"

Connected through hyperedge HE-2 (induced by source paper "de randomized pac bayes margin bounds applications to non convex and non smooth predictors"):
  with members:
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "size free generalization bounds for convolutional neural networks"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-3 (induced by source paper "how much over parameterization is sufficient to learn deep relu networks"):
  with members:
    - "two models of double descent for weak features"
    - "beyond linearization on quadratic and higher order approximation of wide neural networks"
    - "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"
    - "over parameterized two level neural networks can learn near optimal feature representations"
    - "convex formulation of overparameterized deep neural networks"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"

Connected through hyperedge HE-4 (induced by source paper "towards understanding the spectral bias of deep learning"):
  with members:
    - "sgd on neural networks learns functions of increasing complexity"
    - "how much over parameterization is sufficient to learn deep relu networks"
    - "an improved analysis of training over parameterized deep neural networks"
    - "frequency principle fourier analysis sheds light on 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"
    - "polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow relu networks"
    - "on learning over parameterized neural networks a functional approximation prospective"

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)