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 evaluation of adversarial perturbations for sequence to sequence models"
  Abstract: Adversarial examples --- perturbations to the input of a model that elicit large changes in the output --- have been shown to be an effective way of assessing the robustness of sequence-to-sequence (seq2seq) models. However, these perturbations only indicate weaknesses in the model if they do not change the input so significantly that it legitimately results in changes in the expected output. This fact has largely been ignored in the evaluations of the growing body of related literature. Using the example of untargeted attacks on machine translation (MT), we propose a new evaluation framework for adversarial attacks on seq2seq models that takes the semantic equivalence of the pre- and post-perturbation input into account. Using this framework, we demonstrate that existing methods may not preserve meaning in general, breaking the aforementioned assumption that source side perturbations should not result in changes in the expected output. We further use this framework to demonstrate that adding additional constraints on attacks allows for adversarial perturbations that are more meaning-preserving, but nonetheless largely change the output sequence. Finally, we show that performing untargeted adversarial training with meaning-preserving attacks is beneficial to the model in terms of adversarial robustness, without hurting test performance. A toolkit implementing our evaluation framework is released at this https URL.

Connected through hyperedge HE-1 (induced by source paper "adv bert bert is not robust on misspellings generating nature adversarial samples on bert"):
  with members:
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "attention is all you need"
    - "hotflip white box adversarial examples for text classification"

Connected through hyperedge HE-2 (induced by source paper "adversarial language games for advanced natural language intelligence"):
  with members:
    - "emergent tool use from multi agent autocurricula"
    - "better automatic evaluation of open domain dialogue systems with contextualized embeddings"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "egg a toolkit for research on emergence of language in games"
    - "robust neural machine translation with doubly adversarial inputs"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "robust machine comprehension models via adversarial training"
    - "planning inference and pragmatics in sequential language games"

Connected through hyperedge HE-3 (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"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "on adversarial examples for character level neural machine translation"
    - "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-4 (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"
    - "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"
    - "robust neural machine translation with doubly adversarial inputs"

Connected through hyperedge HE-5 (induced by source paper "on the use of bert for neural machine translation"):
  with members:
    - "biobert a pre trained biomedical language representation model for biomedical text mining"
    - "bert rediscovers the classical nlp pipeline"
    - "pre trained language model representations for language generation"
    - "improving robustness of machine translation with synthetic noise"
    - "mass masked sequence to sequence pre training for language generation"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"

Connected through hyperedge HE-6 (induced by source paper "an analysis of source side grammatical errors in nmt"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "reference less quality estimation of text simplification systems"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "neural machine translation of text from non native speakers"
    - "understanding back translation at scale"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"

Connected through hyperedge HE-7 (induced by source paper "a reinforced generation of adversarial samples for neural machine translation"):
  with members:
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "on adversarial examples for character level neural machine translation"
    - "a study of reinforcement learning for neural machine translation"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"
    - "synthetic and natural noise both break neural machine translation"

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)