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: "is bert really robust a strong baseline for natural language attack on text classification and entailment"
  Abstract: Machine learning algorithms are often vulnerable to adversarial examples that have imperceptible alterations from the original counterparts but can fool the state-of-the-art models. It is helpful to evaluate or even improve the robustness of these models by exposing the maliciously crafted adversarial examples. In this paper, we present TextFooler, a simple but strong baseline to generate natural adversarial text. By applying it to two fundamental natural language tasks, text classification and textual entailment, we successfully attacked three target models, including the powerful pre-trained BERT, and the widely used convolutional and recurrent neural networks. We demonstrate the advantages of this framework in three ways: (1) effective---it outperforms state-of-the-art attacks in terms of success rate and perturbation rate, (2) utility-preserving---it preserves semantic content and grammaticality, and remains correctly classified by humans, and (3) efficient---it generates adversarial text with computational complexity linear to the text length. *The code, pre-trained target models, and test examples are available at this https URL.

Connected through hyperedge HE-1 (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"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"

Connected through hyperedge HE-2 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks"):
  with members:
    - "huggingface s transformers state of the art natural language processing"
    - "universal transformers"
    - "a multiscale visualization of attention in the transformer model"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "probing neural network comprehension of natural language arguments"
    - "roberta a robustly optimized bert pretraining approach"
    - "on adversarial examples for character level neural machine translation"
    - "tensor2tensor for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "smoothed inference for adversarially trained models"):
  with members:
    - "robust unsupervised neural machine translation with adversarial training"
    - "maxup a simple way to improve generalization of neural network training"
    - "randomized smoothing of all shapes and sizes"
    - "colored noise injection for training adversarially robust neural networks"
    - "enforcing linearity in dnn succours robustness and adversarial image generation"
    - "training robust deep neural networks via adversarial noise propagation"
    - "adversarial policies attacking deep reinforcement learning"
    - "instance adaptive adversarial training improved accuracy tradeoffs in neural nets"

Connected through hyperedge HE-4 (induced by source paper "spatial aware online adversarial perturbations against visual object tracking"):
  with members:
    - "physical adversarial textures that fool visual object tracking"
    - "deeper and wider siamese networks for real time visual tracking"
    - "robust adversarial perturbation on deep proposal based models"
    - "siamese cascaded region proposal networks for real time visual tracking"
    - "seeing isn t believing practical adversarial attack against object detectors"
    - "distractor aware siamese networks for visual object tracking"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "a twofold siamese network for real time object tracking"

Connected through hyperedge HE-5 (induced by source paper "mixup inference better exploiting mixup to defend adversarial attacks"):
  with members:
    - "theoretically principled trade off between robustness and accuracy"
    - "adversarial training for free"
    - "mixmatch a holistic approach to semi supervised learning"
    - "on evaluating adversarial robustness"
    - "data interpolating prediction alternative interpretation of mixup"
    - "adversarial attacks and defences competition"
    - "adversarial attack on graph structured data"
    - "data augmentation by pairing samples for images classification"

Connected through hyperedge HE-6 (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"
    - "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"
    - "bert pre training of deep bidirectional transformers for language understanding"

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)