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: "theory ii landscape of the empirical risk in deep learning"
  Abstract: Previous theoretical work on deep learning and neural network optimization tend to focus on avoiding saddle points and local minima. However, the practical observation is that, at least in the case of the most successful Deep Convolutional Neural Networks (DCNNs), practitioners can always increase the network size to fit the training data (an extreme example would be [1]). The most successful DCNNs such as VGG and ResNets are best used with a degree of "overparametrization". In this work, we characterize with a mix of theory and experiments, the landscape of the empirical risk of overparametrized DCNNs. We first prove in the regression framework the existence of a large number of degenerate global minimizers with zero empirical error (modulo inconsistent equations). The argument that relies on the use of Bezout theorem is rigorous when the RELUs are replaced by a polynomial nonlinearity (which empirically works as well). As described in our Theory III [2] paper, the same minimizers are degenerate and thus very likely to be found by SGD that will furthermore select with higher probability the most robust zero-minimizer. We further experimentally explored and visualized the landscape of empirical risk of a DCNN on CIFAR-10 during the entire training process and especially the global minima. Finally, based on our theoretical and experimental results, we propose an intuitive model of the landscape of DCNN's empirical loss surface, which might not be as complicated as people commonly believe.

Connected through hyperedge HE-1 (induced by source paper "understanding global loss landscape of one hidden layer relu neural networks"):
  with members:
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "learning one hidden layer neural networks under general input distributions"
    - "over parameterized deep neural networks have no strict local minima for any continuous activations"
    - "gradient descent finds global minima of deep neural networks"
    - "on the loss landscape of a class of deep neural networks with no bad local valleys"

Connected through hyperedge HE-2 (induced by source paper "geometry and topology of deep neural networks decision boundaries"):
  with members:
    - "understanding global loss landscape of one hidden layer relu neural networks"
    - "barcodes as summary of objective function s topology"
    - "depth creates no more spurious local minima"
    - "on connected sublevel sets in deep learning"
    - "elimination of all bad local minima in deep learning"
    - "small nonlinearities in activation functions create bad local minima in neural networks"
    - "on decision regions of narrow deep neural networks"
    - "learning one hidden layer neural networks under general input distributions"

Connected through hyperedge HE-3 (induced by source paper "theory iii dynamics and generalization in deep networks a simple solution"):
  with members:
    - "mean field limit of the learning dynamics of multilayer neural networks"
    - "gradient descent maximizes the margin of homogeneous neural networks"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "loss landscapes of regularized linear autoencoders"
    - "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"
    - "algorithmic regularization in learning deep homogeneous models layers are automatically balanced"

Connected through hyperedge HE-4 (induced by source paper "generating accurate pseudo labels via hermite polynomials for ssl confidently"):
  with members:
    - "trainability and data dependent initialization of over parameterized relu neural networks"
    - "on the expressive power of deep polynomial neural networks"
    - "label propagation for deep semi supervised learning"
    - "joint optimization framework for learning with noisy labels"
    - "activation functions comparison of trends in practice and research for deep learning"
    - "saas speed as a supervisor for semi supervised learning"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "learning one hidden layer neural networks with landscape design"

Connected through hyperedge HE-5 (induced by source paper "when can neural networks learn connected decision regions"):
  with members:
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "learning relus via gradient descent"
    - "optimization landscape and expressivity of deep cnns"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "the loss surface of deep and wide neural networks"
    - "the expressive power of neural networks a view from the width"
    - "error bounds for approximations with deep relu networks"
    - "deep vs shallow networks an approximation theory perspective"

Connected through hyperedge HE-6 (induced by source paper "theoretical issues in deep networks approximation optimization and generalization"):
  with members:
    - "nonlinear approximation and deep relu networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "theory of deep learning iib optimization properties of sgd"
    - "theoretical insights into the optimization landscape of over parameterized shallow neural networks"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "how to escape saddle points efficiently"

Connected through hyperedge HE-7 (induced by source paper "layer dynamics of linearised neural nets"):
  with members:
    - "on the optimization of deep networks implicit acceleration by overparameterization"
    - "theory iiib generalization in deep networks"
    - "lstm a search space odyssey"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "theory of deep learning iii explaining the non overfitting puzzle"
    - "identity matters in deep learning"
    - "dynamics of stochastic gradient algorithms"
    - "understanding deep image representations by inverting them"

Connected through hyperedge HE-8 (induced by source paper "a closer look at deep learning heuristics learning rate restarts warmup and distillation"):
  with members:
    - "analysis of dawnbench a time to accuracy machine learning performance benchmark"
    - "accelerating asynchronous stochastic gradient descent for neural machine translation"
    - "three factors influencing minima in sgd"
    - "averaging weights leads to wider optima and better generalization"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "attention is all you need"
    - "snapshot ensembles train 1 get m for free"
    - "don t decay the learning rate increase the batch size"

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)