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: "large scale structure of neural network loss landscapes"
  Abstract: There are many surprising and perhaps counter-intuitive properties of optimization of deep neural networks. We propose and experimentally verify a unified phenomenological model of the loss landscape that incorporates many of them. High dimensionality plays a key role in our model. Our core idea is to model the loss landscape as a set of high dimensional \emph{wedges} that together form a large-scale, inter-connected structure and towards which optimization is drawn. We first show that hyperparameter choices such as learning rate, network width and $L_2$ regularization, affect the path optimizer takes through the landscape in a similar ways, influencing the large scale curvature of the regions the optimizer explores. Finally, we predict and demonstrate new counter-intuitive properties of the loss-landscape. We show an existence of low loss subspaces connecting a set (not only a pair) of solutions, and verify it experimentally. Finally, we analyze recently popular ensembling techniques for deep networks in the light of our model.

Connected through hyperedge HE-1 (induced by source paper "bayesian deep learning and a probabilistic perspective of generalization"):
  with members:
    - "the case for bayesian deep learning"
    - "fantastic generalization measures and where to find them"
    - "output constrained bayesian neural networks"
    - "scipy 1 0 fundamental algorithms for scientific computing in python"
    - "functional variational bayesian neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "evaluating scalable bayesian deep learning methods for robust computer vision"
    - "understanding generalization through visualizations"

Connected through hyperedge HE-2 (induced by source paper "emergent properties of the local geometry of neural loss landscapes"):
  with members:
    - "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"
    - "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-3 (induced by source paper "stiffness a new perspective on generalization in neural networks"):
  with members:
    - "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"
    - "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-4 (induced by source paper "a deep neural network s loss surface contains every low dimensional pattern"):
  with members:
    - "loss surface sightseeing by multi point optimization"
    - "visualizing the loss landscape of neural 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)