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: "eigenvalues of the hessian in deep learning singularity and beyond"
  Abstract: We look at the eigenvalues of the Hessian of a loss function before and after training. The eigenvalue distribution is seen to be composed of two parts, the bulk which is concentrated around zero, and the edges which are scattered away from zero. We present empirical evidence for the bulk indicating how over-parametrized the system is, and for the edges that depend on the input data.

Connected through hyperedge HE-1 (induced by source paper "rethinking parameter counting in deep models effective dimensionality revisited"):
  with members:
    - "fantastic generalization measures and where to find them"
    - "two models of double descent for weak features"
    - "harmless interpolation of noisy data in regression"
    - "deep double descent where bigger models and more data hurt"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "functional variational bayesian neural networks"
    - "detecting extrapolation with local ensembles"
    - "semi flat minima and saddle points by embedding neural networks to overparameterization"

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"
    - "algorithm dependent generalization bounds for overparameterized deep residual 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"
    - "a convergence analysis of gradient descent for deep linear neural networks"
    - "on tighter generalization bound for deep neural networks cnns resnets and beyond"

Connected through hyperedge HE-3 (induced by source paper "understanding and mitigating gradient pathologies in physics informed neural networks"):
  with members:
    - "machine learning in cardiovascular flows modeling predicting arterial blood pressure from non invasive 4d flow mri data using physics informed neural networks"
    - "variational physics informed neural networks for solving partial differential equations"
    - "the mechanics of n player differentiable games"
    - "hidden fluid mechanics a navier stokes informed deep learning framework for assimilating flow visualization data"
    - "covariant compositional networks for learning graphs"
    - "attention is all you need"
    - "cycles in adversarial regularized learning"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"

Connected through hyperedge HE-4 (induced by source paper "hessian based analysis of sgd for deep nets dynamics and generalization"):
  with members:
    - "escaping saddle points with adaptive gradient methods"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "which algorithmic choices matter at which batch sizes insights from a noisy quadratic model"):
  with members:
    - "eigendamage structured pruning in the kronecker factored eigenbasis"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "understanding short horizon bias in stochastic meta optimization"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "an empirical model of large batch training"
    - "neural tangent kernel convergence and generalization in neural networks"
    - "fast approximate natural gradient descent in a kronecker factored eigenbasis"
    - "eigenvalue corrected noisy natural gradient"

Connected through hyperedge HE-6 (induced by source paper "luck matters understanding training dynamics of deep relu networks"):
  with members:
    - "deconstructing lottery tickets zeros signs and the supermask"
    - "a jamming transition from under to over parametrization affects generalization in deep learning"
    - "measuring the intrinsic dimension of objective landscapes"
    - "deep learning is robust to massive label noise"
    - "theoretical analysis of auto rate tuning by batch normalization"
    - "towards understanding the role of over parametrization in generalization of neural networks"
    - "a theoretical framework for deep and locally connected relu network"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "a closer look at the optimization landscapes of generative adversarial networks"):
  with members:
    - "on finding local nash equilibria and only local nash equilibria in zero sum games"
    - "negative eigenvalues of the hessian in deep neural networks"
    - "local saddle point optimization a curvature exploitation approach"
    - "a variational inequality perspective on generative adversarial nets"
    - "on the convergence of gradient based learning in continuous games"
    - "the mechanics of n player differentiable games"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"

Connected through hyperedge HE-8 (induced by source paper "an investigation into neural net optimization via hessian eigenvalue density"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "hessian based analysis of large batch training and robustness to adversaries"
    - "three factors influencing minima in sgd"
    - "gradient descent happens in a tiny subspace"
    - "towards understanding generalization of deep learning perspective of loss landscapes"
    - "visualizing the loss landscape of neural nets"
    - "sharp minima can generalize for deep nets"

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)