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: "revisiting activation regularization for language rnns"
  Abstract: Recurrent neural networks (RNNs) serve as a fundamental building block for many sequence tasks across natural language processing. Recent research has focused on recurrent dropout techniques or custom RNN cells in order to improve performance. Both of these can require substantial modifications to the machine learning model or to the underlying RNN configurations. We revisit traditional regularization techniques, specifically L2 regularization on RNN activations and slowness regularization over successive hidden states, to improve the performance of RNNs on the task of language modeling. Both of these techniques require minimal modification to existing RNN architectures and result in performance improvements comparable or superior to more complicated regularization techniques or custom cell architectures. These regularization techniques can be used without any modification on optimized LSTM implementations such as the NVIDIA cuDNN LSTM.

Connected through hyperedge HE-1 (induced by source paper "the implicit and explicit regularization effects of dropout"):
  with members:
    - "on dropout and nuclear norm regularization"
    - "improved sample complexities for deep networks and robust classification via an all layer margin"
    - "kernel and deep regimes in overparametrized models"
    - "deterministic pac bayesian generalization bounds for deep networks via generalizing noise resilience"
    - "transformer xl attentive language models beyond a fixed length context"
    - "towards explaining the regularization effect of initial large learning rate in training neural networks"
    - "implicit regularization in deep matrix factorization"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"

Connected through hyperedge HE-2 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "improving sequence to sequence learning via optimal transport"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "dropblock a regularization method for convolutional networks"
    - "policy optimization as wasserstein gradient flows"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "measuring and regularizing networks in function space"

Connected through hyperedge HE-3 (induced by source paper "improving neural language modeling via adversarial training"):
  with members:
    - "partially shuffling the training data to improve language models"
    - "representation degeneration problem in training natural language generation models"
    - "fast parametric learning with activation memorization"
    - "learning towards minimum hyperspherical energy"
    - "frage frequency agnostic word representation"
    - "a la carte embedding cheap but effective induction of semantic feature vectors"
    - "an analysis of neural language modeling at multiple scales"
    - "darts differentiable architecture search"

Connected through hyperedge HE-4 (induced by source paper "scaling object detection by transferring classification weights"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "batch normalization is a cause of adversarial vulnerability"
    - "few shot object detection via feature reweighting"
    - "pfdet 2nd place solution to open images challenge 2018 object detection track"
    - "exploring the limits of weakly supervised pretraining"
    - "visual and semantic knowledge transfer for large scale semi supervised object detection"
    - "zero annotation object detection with web knowledge transfer"
    - "revisiting knowledge transfer for training object class detectors"

Connected through hyperedge HE-5 (induced by source paper "adversarial dropout for recurrent neural networks"):
  with members:
    - "towards binary valued gates for robust lstm training"
    - "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"
    - "recurrent dropout without memory loss"
    - "temporal ensembling for semi supervised learning"
    - "dropout with expectation linear regularization"

Connected through hyperedge HE-6 (induced by source paper "accelerating convolutional neural networks via activation map compression"):
  with members:
    - "stochastic activation pruning for robust adversarial defense"
    - "multilayer convolutional sparse modeling pursuit and dictionary learning"
    - "dnn feature map compression using learned representation over gf 2"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "weightless lossy weight encoding for deep neural network compression"
    - "scnn an accelerator for compressed sparse convolutional neural networks"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "thinet a filter level pruning method for deep neural network compression"

Connected through hyperedge HE-7 (induced by source paper "language modeling at scale"):
  with members:
    - "zipf s law in 50 languages its structural pattern linguistic interpretation and cognitive motivation"
    - "large scale language modeling converging on 40gb of text in four hours"
    - "sparcml high performance sparse communication for machine learning"
    - "large scale distributed neural network training through online distillation"
    - "deep contextualized word representations"
    - "an analysis of neural language modeling at multiple scales"
    - "extremely large minibatch sgd training resnet 50 on imagenet in 15 minutes"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-8 (induced by source paper "an analysis of neural language modeling at multiple scales"):
  with members:
    - "deep contextualized word representations"
    - "learning to generate reviews and discovering sentiment"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "fast slow recurrent neural networks"
    - "regularizing and optimizing lstm language models"
    - "dynamic evaluation of neural sequence models"
    - "accurate large minibatch sgd training imagenet in 1 hour"

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.CL(Computation and Language)