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: "the goldilocks zone towards better understanding of neural network loss landscapes"
  Abstract: We explore the loss landscape of fully-connected and convolutional neural networks using random, low-dimensional hyperplanes and hyperspheres. Evaluating the Hessian, $H$, of the loss function on these hypersurfaces, we observe 1) an unusual excess of the number of positive eigenvalues of $H$, and 2) a large value of $\mathrm{Tr}(H) / ||H||$ at a well defined range of configuration space radii, corresponding to a thick, hollow, spherical shell we refer to as the \textit{Goldilocks zone}. We observe this effect for fully-connected neural networks over a range of network widths and depths on MNIST and CIFAR-10 datasets with the $\mathrm{ReLU}$ and $\tanh$ non-linearities, and a similar effect for convolutional networks. Using our observations, we demonstrate a close connection between the Goldilocks zone, measures of local convexity/prevalence of positive curvature, and the suitability of a network initialization. We show that the high and stable accuracy reached when optimizing on random, low-dimensional hypersurfaces is directly related to the overlap between the hypersurface and the Goldilocks zone, and as a corollary demonstrate that the notion of intrinsic dimension is initialization-dependent. We note that common initialization techniques initialize neural networks in this particular region of unusually high convexity/prevalence of positive curvature, and offer a geometric intuition for their success. Furthermore, we demonstrate that initializing a neural network at a number of points and selecting for high measures of local convexity such as $\mathrm{Tr}(H) / ||H||$, number of positive eigenvalues of $H$, or low initial loss, leads to statistically significantly faster training on MNIST. Based on our observations, we hypothesize that the Goldilocks zone contains an unusually high density of suitable initialization configurations.

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"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "emergent properties of the local geometry of neural loss landscapes"):
  with members:
    - "large scale structure of neural network loss landscapes"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "measuring the intrinsic dimension of objective 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-3 (induced by source paper "large scale structure of neural network loss landscapes"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "three factors influencing minima in sgd"
    - "averaging weights leads to wider optima and better generalization"
    - "eigenvalues of the hessian in deep learning singularity and beyond"
    - "snapshot ensembles train 1 get m for free"
    - "don t decay the learning rate increase the batch size"
    - "visualizing the loss landscape of neural nets"

Connected through hyperedge HE-4 (induced by source paper "stiffness a new perspective on generalization in neural networks"):
  with members:
    - "large scale structure of neural network loss landscapes"
    - "measurements of three level hierarchical structure in the outliers in the spectrum of deepnet hessians"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "gradient descent provably optimizes over parameterized neural networks"

Connected through hyperedge HE-5 (induced by source paper "understanding the impact of entropy on policy optimization"):
  with members:
    - "measuring the intrinsic dimension of objective landscapes"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "clipped action policy gradient"
    - "smoothed action value functions for learning gaussian policies"
    - "addressing function approximation error in actor critic methods"
    - "where did my optimum go an empirical analysis of gradient descent optimization in policy gradient methods"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "equivalence between policy gradients and soft q learning"

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)