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: "improving neural language modeling via adversarial training"
  Abstract: Recently, substantial progress has been made in language modeling by using deep neural networks. However, in practice, large scale neural language models have been shown to be prone to overfitting. In this paper, we present a simple yet highly effective adversarial training mechanism for regularizing neural language models. The idea is to introduce adversarial noise to the output embedding layer while training the models. We show that the optimal adversarial noise yields a simple closed-form solution, thus allowing us to develop a simple and time efficient algorithm. Theoretically, we show that our adversarial mechanism effectively encourages the diversity of the embedding vectors, helping to increase the robustness of models. Empirically, we show that our method improves on the single model state-of-the-art results for language modeling on Penn Treebank (PTB) and Wikitext-2, achieving test perplexity scores of 46.01 and 38.07, respectively. When applied to machine translation, our method improves over various transformer-based translation baselines in BLEU scores on the WMT14 English-German and IWSLT14 German-English tasks.

Connected through hyperedge HE-1 (induced by source paper "meta embeddings based on self attention"):
  with members:
    - "time aware large kernel convolutions"
    - "pay less attention with lightweight and dynamic convolutions"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "adaptive attention span in transformers"
    - "weighted transformer network for machine translation"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "self attention with relative position representations"

Connected through hyperedge HE-2 (induced by source paper "maxup a simple way to improve generalization of neural network training"):
  with members:
    - "certified adversarial robustness via randomized smoothing"
    - "randaugment practical automated data augmentation with a reduced search space"
    - "randaugment practical data augmentation with no separate search"
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial examples improve image recognition"
    - "freelb enhanced adversarial training for language understanding"
    - "fixing the train test resolution discrepancy"
    - "efficientnet rethinking model scaling for convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "black box certification with randomized smoothing a functional optimization based framework"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "certified adversarial robustness via randomized smoothing"
    - "certified robustness for top k predictions against adversarial perturbations via randomized smoothing"
    - "adversarially robust generalization just requires more unlabeled data"
    - "provably robust deep learning via adversarially trained smoothed classifiers"
    - "defense against adversarial attacks using feature scattering based adversarial training"
    - "a dual approach to scalable verification of deep networks"
    - "adversarial logit pairing"

Connected through hyperedge HE-4 (induced by source paper "temporal convolutional attention based network for sequence modeling"):
  with members:
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "trellis networks for sequence modeling"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"
    - "conditional image generation with pixelcnn decoders"
    - "wavenet a generative model for raw audio"

Connected through hyperedge HE-5 (induced by source paper "adaptive noise injection a structure expanding regularization for rnn"):
  with members:
    - "direct output connection for a high rank language model"
    - "frage frequency agnostic word representation"
    - "learning to generate reviews and discovering sentiment"
    - "breaking the softmax bottleneck a high rank rnn language model"
    - "on the state of the art of evaluation in neural language models"
    - "regularizing and optimizing lstm language models"
    - "neural language modeling by jointly learning syntax and lexicon"
    - "efficient softmax approximation for gpus"

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)