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: "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
  Abstract: We consider deep classifying neural networks. We expose a structure in the derivative of the logits with respect to the parameters of the model, which is used to explain the existence of outliers in the spectrum of the Hessian. Previous works decomposed the Hessian into two components, attributing the outliers to one of them, the so-called Covariance of gradients. We show this term is not a Covariance but a second moment matrix, i.e., it is influenced by means of gradients. These means possess an additive two-way structure that is the source of the outliers in the spectrum. This structure can be used to approximate the principal subspace of the Hessian using certain "averaging" operations, avoiding the need for high-dimensional eigenanalysis. We corroborate this claim across different datasets, architectures and sample sizes.

Connected through hyperedge HE-1 (induced by source paper "the break even point on optimization trajectories of deep neural networks"):
  with members:
    - "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"
    - "lca loss change allocation for neural network training"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "time matters in regularizing deep networks weight decay and data augmentation affect early learning dynamics matter little near convergence"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "stiffness a new perspective on generalization in neural networks"
    - "the local elasticity of neural networks"
    - "emergent properties of the local geometry of neural loss landscapes"

Connected through hyperedge HE-2 (induced by source paper "hessian based analysis of sgd for deep nets dynamics and generalization"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on the convergence of adam and beyond"
    - "positively scale invariant flatness of relu neural networks"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "exponential convergence time of gradient descent for one dimensional deep linear neural networks"

Connected through hyperedge HE-3 (induced by source paper "asymptotics of wide networks from feynman diagrams"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "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"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "gradient descent finds global minima of deep neural networks"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "gradient descent happens in a tiny subspace"

Connected through hyperedge HE-4 (induced by source paper "emergent properties of the local geometry of neural loss landscapes"):
  with members:
    - "large scale structure of neural network loss landscapes"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "measuring the intrinsic dimension of objective landscapes"
    - "the goldilocks zone towards better understanding of neural network loss landscapes"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "gradient descent happens in a tiny subspace"
    - "eigenvalues of the hessian in deep learning singularity and beyond"

Connected through hyperedge HE-5 (induced by source paper "the asymptotic spectrum of the hessian of dnn throughout training"):
  with members:
    - "dynamics of deep neural networks and neural tangent hierarchy"
    - "freeze and chaos for dnns an ntk view of batch normalization checkerboard and boundary effects"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "on exact computation with an infinitely wide neural net"
    - "gradient descent happens in a tiny subspace"
    - "a convergence theory for deep learning via over parameterization"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-6 (induced by source paper "stiffness a new perspective on generalization in neural networks"):
  with members:
    - "large scale structure of neural network loss landscapes"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "measuring the intrinsic dimension of objective landscapes"
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "gradient descent finds global minima of deep neural networks"
    - "the goldilocks zone towards better understanding of neural network loss landscapes"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-7 (induced by source paper "the full spectrum of deep net hessians at scale dynamics with sample size"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "ambitious data science can be painless"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "implicit self regularization in deep neural networks evidence from random matrix theory and implications for learning"
    - "an empirical model of large batch training"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "three factors influencing minima in sgd"
    - "gradient descent happens in a tiny subspace"

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)