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: "advances in optimizing recurrent networks"
  Abstract: After a more than decade-long period of relatively little research activity in the area of recurrent neural networks, several new developments will be reviewed here that have allowed substantial progress both in understanding and in technical solutions towards more efficient training of recurrent networks. These advances have been motivated by and related to the optimization issues surrounding deep learning. Although recurrent networks are extremely powerful in what they can in principle represent in terms of modelling sequences,their training is plagued by two aspects of the same issue regarding the learning of long-term dependencies. Experiments reported here evaluate the use of clipping gradients, spanning longer time ranges with leaky integration, advanced momentum techniques, using more powerful output probability models, and encouraging sparser gradients to help symmetry breaking and credit assignment. The experiments are performed on text and music data and show off the combined effects of these techniques in generally improving both training and test error.

Connected through hyperedge HE-1 (induced by source paper "heterogeneous multilayer generalized operational perceptron"):
  with members:
    - "improving efficiency in convolutional neural networks with multilinear filters"
    - "layer wise training of deep networks using kernel similarity"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "progressive learning for systematic design of large neural networks"
    - "a survey of model compression and acceleration for deep neural networks"
    - "wide residual networks"
    - "neural architecture search with reinforcement learning"
    - "you only look once unified real time object detection"

Connected through hyperedge HE-2 (induced by source paper "scheduled restart momentum for accelerated stochastic gradient descent"):
  with members:
    - "on the variance of the adaptive learning rate and beyond"
    - "on the convergence of adam and beyond"
    - "understanding generalization through visualizations"
    - "pytorch an imperative style high performance deep learning library"
    - "laplacian smoothing gradient descent"
    - "accelerated gradient descent escapes saddle points faster than gradient descent"
    - "identity mappings in deep residual networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "forecasting industrial aging processes with machine learning methods"):
  with members:
    - "the autofeat python library for automatic feature engineering and selection"
    - "transfer learning for time series classification"
    - "methods for interpreting and understanding deep neural networks"
    - "an overview and comparative analysis of recurrent neural networks for short term load forecasting"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "pixel recurrent neural networks"
    - "deep learning in neural networks"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-5 (induced by source paper "memory attended recurrent network for video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "automatic stance detection using end to end memory networks"
    - "reconstruction network for video captioning"
    - "recurrent fusion network for image captioning"
    - "end to end video captioning with multitask reinforcement learning"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"

Connected through hyperedge HE-6 (induced by source paper "rnns evolving on an equilibrium manifold a panacea for vanishing and exploding gradients"):
  with members:
    - "recurrent neural networks in the eye of differential equations"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"
    - "frage frequency agnostic word representation"
    - "can recurrent neural networks warp time"
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "neural ordinary differential equations"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"):
  with members:
    - "speech commands a dataset for limited vocabulary speech recognition"
    - "stabilizing gradients for deep neural networks via efficient svd parameterization"
    - "kronecker recurrent units"
    - "on the state of the art of evaluation in neural language models"
    - "gated orthogonal recurrent units on learning to forget"
    - "regularizing and optimizing lstm language models"
    - "size independent sample complexity of neural networks"
    - "exploring sparsity in recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "on the variance of the adaptive learning rate and beyond"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "fixup initialization residual learning without normalization"
    - "on the convergence of adam and beyond"
    - "roberta a robustly optimized bert pretraining approach"
    - "a closer look at deep learning heuristics learning rate restarts warmup and distillation"
    - "training tips for the transformer model"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)