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: "trusting svm for piecewise linear cnns"
  Abstract: We present a novel layerwise optimization algorithm for the learning objective of Piecewise-Linear Convolutional Neural Networks (PL-CNNs), a large class of convolutional neural networks. Specifically, PL-CNNs employ piecewise linear non-linearities such as the commonly used ReLU and max-pool, and an SVM classifier as the final layer. The key observation of our approach is that the problem corresponding to the parameter estimation of a layer can be formulated as a difference-of-convex (DC) program, which happens to be a latent structured SVM. We optimize the DC program using the concave-convex procedure, which requires us to iteratively solve a structured SVM problem. This allows to design an optimization algorithm with an optimal learning rate that does not require any tuning. Using the MNIST, CIFAR and ImageNet data sets, we show that our approach always improves over the state of the art variants of backpropagation and scales to large data and large network settings.

Connected through hyperedge HE-1 (induced by source paper "on the decision boundaries of deep neural networks a tropical geometry perspective"):
  with members:
    - "on the geometry of adversarial examples"
    - "on decision regions of narrow deep neural networks"
    - "robustness via curvature regularization and vice versa"
    - "the lottery ticket hypothesis finding sparse trainable neural networks"
    - "tropical geometry of deep neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the decision boundary of deep neural networks"
    - "structured adversarial attack towards general implementation and better interpretability"

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"
    - "proximal backpropagation"
    - "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"
    - "efficient training of very deep neural networks for supervised hashing"

Connected through hyperedge HE-3 (induced by source paper "deep frank wolfe for neural network optimization"):
  with members:
    - "on the convergence of adam and beyond"
    - "smooth loss functions for deep top k classification"
    - "faster convergence generalization in dnns"
    - "online learning rate adaptation with hypergradient descent"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "exploring generalization in deep learning"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "learned optimizers that scale and generalize"

Connected through hyperedge HE-4 (induced by source paper "lifted neural networks"):
  with members:
    - "pca initialized deep neural networks applied to document image analysis"
    - "training neural networks without gradients a scalable admm approach"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "adam a method for stochastic optimization"
    - "sketching as a tool for numerical linear algebra"
    - "distributed optimization of deeply nested systems"

Connected through hyperedge HE-5 (induced by source paper "efficient optimization for rank based loss functions"):
  with members:
    - "training deep neural networks via direct loss minimization"
    - "speeding up convolutional neural networks with low rank expansions"
    - "rich feature hierarchies for accurate object detection and semantic segmentation"
    - "adadelta an adaptive learning rate method"
    - "totally corrective boosting for regularized risk minimization"

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)