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: "on adversarial examples for character level neural machine translation"
  Abstract: Evaluating on adversarial examples has become a standard procedure to measure robustness of deep learning models. Due to the difficulty of creating white-box adversarial examples for discrete text input, most analyses of the robustness of NLP models have been done through black-box adversarial examples. We investigate adversarial examples for character-level neural machine translation (NMT), and contrast black-box adversaries with a novel white-box adversary, which employs differentiable string-edit operations to rank adversarial changes. We propose two novel types of attacks which aim to remove or change a word in a translation, rather than simply break the NMT. We demonstrate that white-box adversarial examples are significantly stronger than their black-box counterparts in different attack scenarios, which show more serious vulnerabilities than previously known. In addition, after performing adversarial training, which takes only 3 times longer than regular training, we can improve the model's robustness significantly.

Connected through hyperedge HE-1 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "cnns found to jump around more skillfully than rnns compositional generalization in seq2seq convolutional networks"
    - "measuring compositionality in representation learning"
    - "the emergence of number and syntax units in lstm language models"
    - "what do rnn language models learn about filler gap dependencies"
    - "distinct patterns of syntactic agreement errors in recurrent networks and humans"
    - "jump to better conclusions scan both left and right"
    - "deep learning a critical appraisal"

Connected through hyperedge HE-2 (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"
    - "tensor2tensor for neural machine translation"

Connected through hyperedge HE-3 (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"
    - "adversarial example generation with syntactically controlled paraphrase networks"

Connected through hyperedge HE-4 (induced by source paper "don t take the premise for granted mitigating artifacts in natural language inference"):
  with members:
    - "adversarial regularization for visual question answering strengths shortcomings and side effects"
    - "on adversarial removal of hypothesis only bias in natural language inference"
    - "annotation artifacts in natural language inference data"
    - "collecting diverse natural language inference problems for sentence representation evaluation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "analysis methods in neural language processing a survey"
    - "adventure adversarial training for textual entailment with knowledge guided examples"
    - "overcoming language priors in visual question answering with adversarial regularization"

Connected through hyperedge HE-5 (induced by source paper "test metrics for recurrent neural networks"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "recurrent neural networks for time series forecasting"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "symbolic execution for deep neural networks"
    - "towards dependability metrics for neural networks"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "universal adversarial triggers for attacking and analyzing nlp"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "annotation artifacts in natural language inference data"
    - "adversarial patch"
    - "what makes reading comprehension questions easier"
    - "hypothesis only baselines in natural language inference"

Connected through hyperedge HE-7 (induced by source paper "findings of the first shared task on machine translation robustness"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "improving robustness of machine translation with synthetic noise"
    - "ntt s machine translation systems for wmt19 robustness task"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "cuni system for the wmt19 robustness task"
    - "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "training on synthetic noise improves robustness to natural noise in machine translation"

Connected through hyperedge HE-8 (induced by source paper "robust neural machine translation with doubly adversarial inputs"):
  with members:
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "towards robust neural machine translation"
    - "generating natural language adversarial examples"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "training deeper neural machine translation models with transparent attention"
    - "mtnt a testbed for machine translation of noisy text"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "understanding back translation at scale"

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)