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: "deep text classification can be fooled"
  Abstract: In this paper, we present an effective method to craft text adversarial samples, revealing one important yet underestimated fact that DNN-based text classifiers are also prone to adversarial sample attack. Specifically, confronted with different adversarial scenarios, the text items that are important for classification are identified by computing the cost gradients of the input (white-box attack) or generating a series of occluded test samples (black-box attack). Based on these items, we design three perturbation strategies, namely insertion, modification, and removal, to generate adversarial samples. The experiment results show that the adversarial samples generated by our method can successfully fool both state-of-the-art character-level and word-level DNN-based text classifiers. The adversarial samples can be perturbed to any desirable classes without compromising their utilities. At the same time, the introduced perturbation is difficult to be perceived.

Connected through hyperedge HE-1 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning"):
  with members:
    - "towards a robust deep neural network in text domain a survey"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "cross lingual alignment of contextual word embeddings with applications to zero shot dependency parsing"
    - "understanding the origins of bias in word embeddings"
    - "adversarial attacks on node embeddings via graph poisoning"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "towards understanding linear word analogies"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "adversarial robustness for code"):
  with members:
    - "deep gamblers learning to abstain with portfolio theory"
    - "certified robustness to adversarial word substitutions"
    - "inferring javascript types using graph neural networks"
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "universal transformers"
    - "adversarial attacks on graph neural networks via meta learning"
    - "structured neural summarization"
    - "code2vec learning distributed representations of code"

Connected through hyperedge HE-3 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world"):
  with members:
    - "towards a human like open domain chatbot"
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "mass masked sequence to sequence pre training for language generation"
    - "sequence embeddings help to identify fraudulent cases in healthcare insurance"
    - "soft edit distance for differentiable comparison of symbolic sequences"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"

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"
    - "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"
    - "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"
    - "textbugger generating adversarial text against real world applications"
    - "contextual augmentation data augmentation by words with paradigmatic relations"
    - "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"
    - "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"
    - "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 "playing to learn better repeated games for adversarial learning with multiple classifiers"):
  with members:
    - "enhancing robustness of deep neural networks against adversarial malware samples principles framework and aics 2019 challenge"
    - "adversarial machine learning at scale"
    - "character level convolutional networks for text classification"

Connected through hyperedge HE-8 (induced by source paper "adversarial examples for deep learning cyber security analytics"):
  with members:
    - "robust audio adversarial example for a physical attack"
    - "certified adversarial robustness via randomized smoothing"
    - "on designing machine learning models for malicious network traffic classification"
    - "rallying adversarial techniques against deep learning for network security"
    - "evading classifiers in discrete domains with provable optimality guarantees"
    - "kitsune an ensemble of autoencoders for online network intrusion detection"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deceiving end to end deep learning malware detectors using adversarial examples"

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)