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: "how do infinite width bounded norm networks look in function space"
  Abstract: We consider the question of what functions can be captured by ReLU networks with an unbounded number of units (infinite width), but where the overall network Euclidean norm (sum of squares of all weights in the system, except for an unregularized bias term for each unit) is bounded; or equivalently what is the minimal norm required to approximate a given function. For functions $f : \mathbb R \rightarrow \mathbb R$ and a single hidden layer, we show that the minimal network norm for representing $f$ is $\max(\int |f''(x)| dx, |f'(-\infty) + f'(+\infty)|)$, and hence the minimal norm fit for a sample is given by a linear spline interpolation.

Connected through hyperedge HE-1 (induced by source paper "balancedness and alignment are unlikely in linear neural networks"):
  with members:
    - "memorization in overparameterized autoencoders"
    - "global convergence of adaptive gradient methods for an over parameterized neural network"
    - "implicit regularization in deep matrix factorization"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "gradient descent aligns the layers of deep linear networks"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-2 (induced by source paper "neural networks are convex regularizers exact polynomial time convex optimization formulations for two layer networks"):
  with members:
    - "on exact computation with an infinitely wide neural net"
    - "complexity of training relu neural networks"
    - "principled deep neural network training through linear programming"
    - "a convergence theory for deep learning via over parameterization"
    - "gradient descent provably optimizes over parameterized neural networks"
    - "reliably learning the relu in polynomial time"
    - "understanding deep neural networks with rectified linear units"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "convex geometry and duality of over parameterized neural networks"):
  with members:
    - "gradient dynamics of shallow univariate relu networks"
    - "on exact computation with an infinitely wide neural net"
    - "implicit regularization for deep neural networks driven by an ornstein uhlenbeck like process"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"
    - "breaking the curse of dimensionality with convex neural networks"

Connected through hyperedge HE-4 (induced by source paper "kernel and deep regimes in overparametrized models"):
  with members:
    - "exponentiated gradient meets gradient descent"
    - "scaling limits of wide neural networks with weight sharing gaussian process behavior gradient independence and neural tangent kernel derivation"
    - "disentangling feature and lazy learning in deep neural networks an empirical study"
    - "implicit regularization in deep matrix factorization"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "implicit bias of gradient descent on linear convolutional networks"
    - "neural tangent kernel convergence and generalization in neural networks"

Connected through hyperedge HE-5 (induced by source paper "active learning in the overparameterized and interpolating regime"):
  with members:
    - "two models of double descent for weak features"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "fast and faster convergence of sgd for over parameterized models and an accelerated perceptron"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "deep active learning for named entity recognition"
    - "cost effective active learning for deep image classification"
    - "a pac bayesian approach to spectrally normalized margin bounds for neural networks"
    - "the power of interpolation understanding the effectiveness of sgd in modern over parametrized learning"

Connected through hyperedge HE-6 (induced by source paper "approximation power of random neural networks"):
  with members:
    - "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"
    - "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"
    - "towards understanding the role of over parametrization in generalization of neural networks"

Connected through hyperedge HE-7 (induced by source paper "a hessian based complexity measure for deep networks"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "two models of double descent for weak features"
    - "on exact computation with an infinitely wide neural net"
    - "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"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "a kernel theory of modern data augmentation"
    - "stronger generalization bounds for deep nets via a compression approach"

Connected through hyperedge HE-8 (induced by source paper "memorization in overparameterized autoencoders"):
  with members:
    - "greedy layerwise learning can scale to imagenet"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "deja vu an empirical evaluation of the memorization properties of convnets"
    - "deep image prior"
    - "sgd learns over parameterized networks that provably generalize on linearly separable data"
    - "understanding deep learning requires rethinking generalization"
    - "u net convolutional networks for biomedical image segmentation"

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)