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: "towards crafting text adversarial samples"
  Abstract: Adversarial samples are strategically modified samples, which are crafted with the purpose of fooling a classifier at hand. An attacker introduces specially crafted adversarial samples to a deployed classifier, which are being mis-classified by the classifier. However, the samples are perceived to be drawn from entirely different classes and thus it becomes hard to detect the adversarial samples. Most of the prior works have been focused on synthesizing adversarial samples in the image domain. In this paper, we propose a new method of crafting adversarial text samples by modification of the original samples. Modifications of the original text samples are done by deleting or replacing the important or salient words in the text or by introducing new words in the text sample. Our algorithm works best for the datasets which have sub-categories within each of the classes of examples. While crafting adversarial samples, one of the key constraint is to generate meaningful sentences which can at pass off as legitimate from language (English) viewpoint. Experimental results on IMDB movie review dataset for sentiment analysis and Twitter dataset for gender detection show the efficiency of our proposed method.

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"
    - "deep text classification can be fooled"
    - "towards understanding linear word analogies"

Connected through hyperedge HE-2 (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-3 (induced by source paper "g odel s sentence is an adversarial example but unsolvable"):
  with members:
    - "adversarial robustness may be at odds with simplicity"
    - "adversarial examples are a natural consequence of test error in noise"
    - "text processing like humans do visually attacking and shielding nlp systems"
    - "natural adversarial examples"
    - "on the sensitivity of adversarial robustness to input data distributions"
    - "fooling automated surveillance cameras adversarial patches to attack person detection"
    - "probing neural network comprehension of natural language arguments"
    - "adversarial attacks on deep learning models in natural language processing a survey"

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"
    - "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-5 (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"
    - "adversarial texts with gradient methods"
    - "crafting adversarial input sequences for recurrent neural networks"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-6 (induced by source paper "upc learning universal physical camouflage attacks on object detectors"):
  with members:
    - "humans can decipher adversarial images"
    - "robust adversarial perturbation on deep proposal based models"
    - "did you hear that adversarial examples against automatic speech recognition"
    - "textbugger generating adversarial text against real world applications"
    - "yolov3 an incremental improvement"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "dpatch an adversarial patch attack on object detectors"
    - "black box adversarial attacks with limited queries and information"

Connected through hyperedge HE-7 (induced by source paper "rnn test adversarial testing framework for recurrent neural network systems"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "deepcruiser automated guided testing for stateful deep learning systems"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deep text classification can be fooled"
    - "interpretable adversarial perturbation in input embedding space for text"
    - "audio adversarial examples targeted attacks on speech to text"
    - "dlfuzz differential fuzzing testing of deep learning systems"
    - "combinatorial testing for deep learning systems"

Connected through hyperedge HE-8 (induced by source paper "advcodec towards a unified framework for adversarial text generation"):
  with members:
    - "achieving verified robustness to symbol substitutions via interval bound propagation"
    - "universal adversarial triggers for attacking and analyzing nlp"
    - "is bert really robust a strong baseline for natural language attack on text classification and entailment"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "textbugger generating adversarial text against real world applications"
    - "deep text classification can be fooled"
    - "generating natural language adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with 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.LG(Machine Learning)