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: "towards binary valued gates for robust lstm training"
  Abstract: Long Short-Term Memory (LSTM) is one of the most widely used recurrent structures in sequence modeling. It aims to use gates to control information flow (e.g., whether to skip some information or not) in the recurrent computations, although its practical implementation based on soft gates only partially achieves this goal. In this paper, we propose a new way for LSTM training, which pushes the output values of the gates towards 0 or 1. By doing so, we can better control the information flow: the gates are mostly open or closed, instead of in a middle state, which makes the results more interpretable. Empirical studies show that (1) Although it seems that we restrict the model capacity, there is no performance drop: we achieve better or comparable performances due to its better generalization ability; (2) The outputs of gates are not sensitive to their inputs: we can easily compress the LSTM unit in multiple ways, e.g., low-rank approximation and low-precision approximation. The compressed models are even better than the baseline models without compression.

Connected through hyperedge HE-1 (induced by source paper "training progressively binarizing deep networks using fpgas"):
  with members:
    - "accelerating deterministic and stochastic binarized neural networks on fpgas using opencl"
    - "self binarizing networks"
    - "generalized cross entropy loss for training deep neural networks with noisy labels"
    - "bnn improved binary network training"
    - "towards accurate binary convolutional neural network"
    - "hashnet deep learning to hash by continuation"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "improving the gating mechanism of recurrent neural networks"):
  with members:
    - "towards non saturating recurrent units for modelling long term dependencies"
    - "transformer xl attentive language models beyond a fixed length context"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "fast parametric learning with activation memorization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "optimizing agent behavior over long time scales by transporting value"
    - "trellis networks for sequence modeling"
    - "can recurrent neural networks warp time"

Connected through hyperedge HE-3 (induced by source paper "learning in gated neural networks"):
  with members:
    - "can sgd learn recurrent neural networks with provable generalization"
    - "non asymptotic identification of lti systems from a single trajectory"
    - "robust spectral filtering and anomaly detection"
    - "learning one hidden layer neural networks under general input distributions"
    - "breaking the gridlock in mixture of experts consistent and efficient algorithms"
    - "on the convergence rate of training recurrent neural networks"
    - "learning without mixing towards a sharp analysis of linear system identification"
    - "learning one hidden layer neural networks with landscape design"

Connected through hyperedge HE-4 (induced by source paper "armin towards a more efficient and light weight recurrent memory network"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "sparse attentive backtracking temporal creditassignment through reminding"
    - "fast slow recurrent neural networks"
    - "memory augmented neural networks with wormhole connections"
    - "skip rnn learning to skip state updates in recurrent neural networks"
    - "associative long short term memory"
    - "scaling memory augmented neural networks with sparse reads and writes"
    - "the concrete distribution a continuous relaxation of discrete random variables"

Connected through hyperedge HE-5 (induced by source paper "bivariate beta lstm"):
  with members:
    - "r transformer recurrent neural network enhanced transformer"
    - "complex gated recurrent neural networks"
    - "image captioning with semantic attention"
    - "spice semantic propositional image caption evaluation"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"
    - "recurrent batch normalization"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "adversarial dropout for recurrent neural networks"):
  with members:
    - "towards crafting text adversarial samples"
    - "on the state of the art of evaluation in neural language models"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "regularizing and optimizing lstm language models"
    - "revisiting activation regularization for language rnns"
    - "recurrent dropout without memory loss"
    - "temporal ensembling for semi supervised learning"
    - "dropout with expectation linear regularization"

Connected through hyperedge HE-7 (induced by source paper "hierarchical context enabled recurrent neural network for recommendation"):
  with members:
    - "contextual sequence modeling for recommendation with recurrent neural networks"
    - "attention is all you need"
    - "topically driven neural language model"
    - "discovering discrete latent topics with neural variational inference"
    - "phased lstm accelerating recurrent network training for long or event based sequences"
    - "improved recurrent neural networks for session based recommendations"
    - "topicrnn a recurrent neural network with long range semantic dependency"
    - "session based recommendations with recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "quantization loss re learning method"):
  with members:
    - "optimal hyperparameters for deep lstm networks for sequence labeling tasks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "bitwise neural networks"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "bidirectional lstm crf models for sequence tagging"
    - "training deep neural networks with low precision multiplications"

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)