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: "adversarial example generation with syntactically controlled paraphrase networks"
  Abstract: We propose syntactically controlled paraphrase networks (SCPNs) and use them to generate adversarial examples. Given a sentence and a target syntactic form (e.g., a constituency parse), SCPNs are trained to produce a paraphrase of the sentence with the desired syntax. We show it is possible to create training data for this task by first doing backtranslation at a very large scale, and then using a parser to label the syntactic transformations that naturally occur during this process. Such data allows us to train a neural encoder-decoder model with extra inputs to specify the target syntax. A combination of automated and human evaluations show that SCPNs generate paraphrases that follow their target specifications without decreasing paraphrase quality when compared to baseline (uncontrolled) paraphrase systems. Furthermore, they are more capable of generating syntactically adversarial examples that both (1) "fool" pretrained models and (2) improve the robustness of these models to syntactic variation when used to augment their training data.

Connected through hyperedge HE-1 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks"):
  with members:
    - "huggingface s transformers state of the art natural language processing"
    - "universal transformers"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "roberta a robustly optimized bert pretraining approach"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "freelb enhanced adversarial training for natural language understanding"):
  with members:
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "adversarial attacks on copyright detection systems"
    - "multi task deep neural networks for natural language understanding"
    - "albert a lite bert for self supervised learning of language representations"
    - "robust neural machine translation with doubly adversarial inputs"
    - "roberta a robustly optimized bert pretraining approach"
    - "can a suit of armor conduct electricity a new dataset for open book question answering"
    - "feature denoising for improving adversarial robustness"

Connected through hyperedge HE-3 (induced by source paper "robustness to programmable string transformations via augmented abstract training"):
  with members:
    - "popqorn quantifying robustness of recurrent neural networks"
    - "combating adversarial misspellings with robust word recognition"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "generating natural adversarial examples"
    - "robsut wrod reocginiton via semi character recurrent neural network"
    - "adam a method for stochastic optimization"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-4 (induced by source paper "structure invariant testing for machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "defensive quantization when efficiency meets robustness"
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "sirenattack generating adversarial audio for end to end acoustic systems"
    - "achieving human parity on automatic chinese to english news translation"
    - "code2vec learning distributed representations of code"
    - "on adversarial examples for character level neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "quizbowl the case for incremental question answering"):
  with members:
    - "multinomial adversarial networks for multi domain text classification"
    - "annotation artifacts in natural language inference data"
    - "what can ai do for me evaluating machine learning interpretations in cooperative play"
    - "hotpotqa a dataset for diverse explainable multi hop question answering"
    - "what makes reading comprehension questions easier"
    - "studio ousia s quiz bowl question answering system"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-6 (induced by source paper "controllable paraphrase generation with a syntactic exemplar"):
  with members:
    - "a multi task approach for disentangling syntax and semantics in sentence representations"
    - "toward unsupervised text content manipulation"
    - "text generation with exemplar based adaptive decoding"
    - "smaller text classifiers with discriminative cluster embeddings"
    - "variational sequential labelers for semi supervised learning"
    - "towards generating long and coherent text with multi level latent variable models"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "disentangled representation learning for non parallel text style transfer"

Connected through hyperedge HE-7 (induced by source paper "generating black box adversarial examples for text classifiers using a deep reinforced model"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "spatially transformed adversarial examples"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "gray box adversarial training"
    - "adversarial texts with gradient methods"
    - "motivating the rules of the game for adversarial example research"
    - "a continuously growing dataset of sentential paraphrases"

Connected through hyperedge HE-8 (induced by source paper "a multi task approach for disentangling syntax and semantics in sentence representations"):
  with members:
    - "variational sequential labelers for semi supervised learning"
    - "disentangled representation learning for non parallel text style transfer"
    - "deep contextualized word representations"
    - "spherical latent spaces for stable variational autoencoders"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "multimodal generative models for scalable weakly supervised learning"
    - "toward controlled generation of text"

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)