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: "textbugger generating adversarial text against real world applications"
  Abstract: Deep Learning-based Text Understanding (DLTU) is the backbone technique behind various applications, including question answering, machine translation, and text classification. Despite its tremendous popularity, the security vulnerabilities of DLTU are still largely unknown, which is highly concerning given its increasing use in security-sensitive applications such as sentiment analysis and toxic content detection. In this paper, we show that DLTU is inherently vulnerable to adversarial text attacks, in which maliciously crafted texts trigger target DLTU systems and services to misbehave. Specifically, we present TextBugger, a general attack framework for generating adversarial texts. In contrast to prior works, TextBugger differs in significant ways: (i) effective -- it outperforms state-of-the-art attacks in terms of attack success rate; (ii) evasive -- it preserves the utility of benign text, with 94.9\% of the adversarial text correctly recognized by human readers; and (iii) efficient -- it generates adversarial text with computational complexity sub-linear to the text length. We empirically evaluate TextBugger on a set of real-world DLTU systems and services used for sentiment analysis and toxic content detection, demonstrating its effectiveness, evasiveness, and efficiency. For instance, TextBugger achieves 100\% success rate on the IMDB dataset based on Amazon AWS Comprehend within 4.61 seconds and preserves 97\% semantic similarity. We further discuss possible defense mechanisms to mitigate such attack and the adversary's potential countermeasures, which leads to promising directions for further research.

Connected through hyperedge HE-1 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks"):
  with members:
    - "fastwordbug a fast method to generate adversarial text against nlp applications"
    - "transferability of adversarial examples to attack cloud based image classifier service"
    - "robust audio adversarial example for a physical attack"
    - "daedalus breaking non maximum suppression in object detection via adversarial examples"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "a tour of convolutional networks guided by linear interpreters"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "adversarial t shirt evading person detectors in a physical world"

Connected through hyperedge HE-2 (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"
    - "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 texts with gradient methods"
    - "adversarial attack and defense on graph data a survey"

Connected through hyperedge HE-3 (induced by source paper "conaml constrained adversarial machine learning for cyber physical systems"):
  with members:
    - "dynamic detection of false data injection attack in smart grid using deep learning"
    - "adversarial attacks and defences a survey"
    - "learning from mutants using code mutation to learn and monitor invariants of a cyber physical system"
    - "adversarial regression for detecting attacks in cyber physical systems"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "a deep learning based framework for conducting stealthy attacks in industrial control systems"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "deeptest automated testing of deep neural network driven autonomous cars"

Connected through hyperedge HE-4 (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"
    - "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"
    - "poison frogs targeted clean label poisoning attacks on neural networks"

Connected through hyperedge HE-5 (induced by source paper "benchmarking popular classification models robustness to random and targeted corruptions"):
  with members:
    - "model vulnerability to distributional shifts over image transformation sets"
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "deep text classification can be fooled"
    - "deepmutation mutation testing of deep learning systems"
    - "generating natural language adversarial examples"
    - "allennlp a deep semantic natural language processing platform"

Connected through hyperedge HE-6 (induced by source paper "adv bert bert is not robust on misspellings generating nature adversarial samples on bert"):
  with members:
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "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-7 (induced by source paper "exploring and improving robustness of multi task deep neural networks via domain agnostic defenses"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adapting sequence models for sentence correction"
    - "stochastic answer networks for machine reading comprehension"
    - "hotflip white box adversarial examples for text classification"

Connected through hyperedge HE-8 (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"
    - "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"
    - "adversarial texts with gradient methods"
    - "attention is all you need"
    - "hotflip white box adversarial examples for text classification"
    - "towards evaluating the robustness of neural networks"

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.CR(Cryptography and Security)