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: "proportionate gradient updates with percentdelta"
  Abstract: Deep Neural Networks are generally trained using iterative gradient updates. Magnitudes of gradients are affected by many factors, including choice of activation functions and initialization. More importantly, gradient magnitudes can greatly differ across layers, with some layers receiving much smaller gradients than others. causing some layers to train slower than others and therefore slowing down the overall convergence. We analytically explain this disproportionality. Then we propose to explicitly train all layers at the same speed, by scaling the gradient w.r.t. every trainable tensor to be proportional to its current value. In particular, at every batch, we want to update all trainable tensors, such that the relative change of the L1-norm of the tensors is the same, across all layers of the network, throughout training time. Experiments on MNIST show that our method appropriately scales gradients, such that the relative change in trainable tensors is approximately equal across layers. In addition, measuring the test accuracy with training time, shows that our method trains faster than other methods, giving higher test accuracy given same budget of training steps.

Connected through hyperedge HE-1 (induced by source paper "convergence analysis of gradient descent algorithms with proportional updates"):
  with members:
    - "scaling sgd batch size to 32k for imagenet training"
    - "identity mappings in deep residual networks"
    - "densely connected convolutional networks"
    - "an overview of gradient descent optimization algorithms"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "visualizing and understanding convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "block mean approximation for efficient second order optimization"):
  with members:
    - "adam a method for stochastic optimization"
    - "new insights and perspectives on the natural gradient method"
    - "riemannian metrics for neural networks i feedforward networks"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-3 (induced by source paper "learning edge representations via low rank asymmetric projections"):
  with members:
    - "harp hierarchical representation learning for networks"
    - "gated graph sequence neural networks"
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "don t walk skip online learning of multi scale network embeddings"
    - "discriminative embeddings of latent variable models for structured data"
    - "deep convolutional networks on graph structured data"

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)