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: "proximal backpropagation"
  Abstract: We propose proximal backpropagation (ProxProp) as a novel algorithm that takes implicit instead of explicit gradient steps to update the network parameters during neural network training. Our algorithm is motivated by the step size limitation of explicit gradient descent, which poses an impediment for optimization. ProxProp is developed from a general point of view on the backpropagation algorithm, currently the most common technique to train neural networks via stochastic gradient descent and variants thereof. Specifically, we show that backpropagation of a prediction error is equivalent to sequential gradient descent steps on a quadratic penalty energy, which comprises the network activations as variables of the optimization. We further analyze theoretical properties of ProxProp and in particular prove that the algorithm yields a descent direction in parameter space and can therefore be combined with a wide variety of convergent algorithms. Finally, we devise an efficient numerical implementation that integrates well with popular deep learning frameworks. We conclude by demonstrating promising numerical results and show that ProxProp can be effectively combined with common first order optimizers such as Adam.

Connected through hyperedge HE-1 (induced by source paper "layer wise conditioning analysis in exploring the learning dynamics of dnns"):
  with members:
    - "a mean field theory of batch normalization"
    - "mean field analysis of batch normalization"
    - "an investigation into neural net optimization via hessian eigenvalue density"
    - "l1 norm batch normalization for efficient training of deep neural networks"
    - "group normalization"
    - "the full spectrum of deep net hessians at scale dynamics with sample size"
    - "large scale gan training for high fidelity natural image synthesis"
    - "empirical analysis of the hessian of over parametrized neural networks"

Connected through hyperedge HE-2 (induced by source paper "contrastive learning for lifted networks"):
  with members:
    - "lifted proximal operator machines"
    - "fenchel lifted networks a lagrange relaxation of neural network training"
    - "lifted neural networks"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "training neural networks without gradients a scalable admm approach"
    - "equilibrium propagation bridging the gap between energy based models and backpropagation"
    - "trusting svm for piecewise linear cnns"
    - "efficient training of very deep neural networks for supervised hashing"

Connected through hyperedge HE-3 (induced by source paper "regularization for deep learning a taxonomy"):
  with members:
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "deep unsupervised learning through spatial contrasting"
    - "adjusting for dropout variance in batch normalization and weight initialization"
    - "synthesizing robust adversarial examples"
    - "an overview of multi task learning in deep neural networks"
    - "densely connected convolutional networks"
    - "trivializing the energy landscape of deep networks"
    - "multi scale context aggregation by dilated convolutions"

Connected through hyperedge HE-4 (induced by source paper "constrained deep learning using conditional gradient and applications in computer vision"):
  with members:
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "beating the perils of non convexity guaranteed training of neural networks using tensor methods"
    - "imposing hard constraints on deep networks promises and limitations"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "an overview of multi task learning in deep neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "low rank passthrough neural networks"

Connected through hyperedge HE-5 (induced by source paper "a unified framework for training neural networks"):
  with members:
    - "recovery guarantees for one hidden layer neural networks"
    - "depth creates no bad local minima"
    - "an analytical formula of population gradient for two layered relu network and its applications in convergence and critical point analysis"
    - "training neural networks without gradients a scalable admm approach"
    - "beyond convexity stochastic quasi convex optimization"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-6 (induced by source paper "the lov asz softmax loss a tractable surrogate for the optimization of the intersection over union measure in neural networks"):
  with members:
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "simultaneous detection and segmentation"
    - "microsoft coco common objects in context"
    - "fully convolutional networks for semantic segmentation"

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)