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 texts with gradient methods"
  Abstract: Adversarial samples for images have been extensively studied in the literature. Among many of the attacking methods, gradient-based methods are both effective and easy to compute. In this work, we propose a framework to adapt the gradient attacking methods on images to text domain. The main difficulties for generating adversarial texts with gradient methods are i) the input space is discrete, which makes it difficult to accumulate small noise directly in the inputs, and ii) the measurement of the quality of the adversarial texts is difficult. We tackle the first problem by searching for adversarials in the embedding space and then reconstruct the adversarial texts via nearest neighbor search. For the latter problem, we employ the Word Mover's Distance (WMD) to quantify the quality of adversarial texts. Through extensive experiments on three datasets, IMDB movie reviews, Reuters-2 and Reuters-5 newswires, we show that our framework can leverage gradient attacking methods to generate very high-quality adversarial texts that are only a few words different from the original texts. There are many cases where we can change one word to alter the label of the whole piece of text. We successfully incorporate FGM and DeepFool into our framework. In addition, we empirically show that WMD is closely related to the quality of adversarial texts.

Connected through hyperedge HE-1 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling"):
  with members:
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "adversarial attack on graph structured data"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "neural nearest neighbors networks"
    - "generating adversarial examples with adversarial networks"
    - "adversarial attack and defense on graph data a survey"

Connected through hyperedge HE-2 (induced by source paper "to transfer or not to transfer misclassification attacks against transfer learned text classifiers"):
  with members:
    - "towards a robust deep neural network in text domain a survey"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "taskonomy disentangling task transfer learning"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "fine tuned language models for text classification"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-3 (induced by source paper "elephant in the room an evaluation framework for assessing adversarial examples in nlp"):
  with members:
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "generating adversarial examples with adversarial networks"
    - "attention is all you need"
    - "hotflip white box adversarial examples for text classification"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-4 (induced by source paper "fastwordbug a fast method to generate adversarial text against nlp applications"):
  with members:
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "towards crafting text adversarial samples"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-5 (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"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "gray box adversarial training"
    - "motivating the rules of the game for adversarial example research"
    - "a continuously growing dataset of sentential paraphrases"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples with difficult common words for paraphrase identification"):
  with members:
    - "paws paraphrase adversaries from word scrambling"
    - "paws x a cross lingual adversarial dataset for paraphrase identification"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "robust machine comprehension models via adversarial training"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "breaking nli systems with sentences that require simple lexical inferences"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-7 (induced by source paper "interpretable adversarial training for text"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "are adversarial examples inevitable"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "universal language model fine tuning for text classification"
    - "adversarially regularized autoencoders"
    - "synthetic and natural noise both break neural machine translation"
    - "adversarial examples for evaluating reading comprehension systems"

Connected through hyperedge HE-8 (induced by source paper "generalizable adversarial attacks using generative models"):
  with members:
    - "nattack learning the distributions of adversarial examples for an improved black box attack on deep neural networks"
    - "adversarial attack on graph structured data"
    - "textbugger generating adversarial text against real world applications"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "generating natural language adversarial examples"
    - "neural nearest neighbors networks"
    - "generating adversarial examples with adversarial networks"
    - "adversarial attack and defense on graph data a survey"

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)