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 a robust deep neural network in text domain a survey"
  Abstract: Deep neural networks (DNNs) have shown an inherent vulnerability to adversarial examples which are maliciously crafted on real examples by attackers, aiming at making target DNNs misbehave. The threats of adversarial examples are widely existed in image, voice, speech, and text recognition and classification. Inspired by the previous work, researches on adversarial attacks and defenses in text domain develop rapidly. In order to make people have a general understanding about the field, this article presents a comprehensive review on adversarial examples in text. We analyze the advantages and shortcomings of recent adversarial examples generation methods and elaborate the efficiency and limitations on countermeasures. Finally, we discuss the challenges in adversarial texts and provide a research direction of this aspect.

Connected through hyperedge HE-1 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning"):
  with members:
    - "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"
    - "generating natural language adversarial examples"

Connected through hyperedge HE-2 (induced by source paper "to transfer or not to transfer misclassification attacks against transfer learned text classifiers"):
  with members:
    - "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"
    - "poison frogs targeted clean label poisoning attacks on neural networks"

Connected through hyperedge HE-3 (induced by source paper "enhancing recurrent neural networks with sememes"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "leveraging knowledge bases in lstms for improving machine reading"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "incorporating chinese characters of words for lexical sememe prediction"
    - "language modeling with sparse product of sememe experts"
    - "generating natural language adversarial examples"
    - "jointly predicting predicates and arguments in neural semantic role labeling"
    - "extending neural generative conversational model using external knowledge sources"

Connected through hyperedge HE-4 (induced by source paper "learning to discriminate perturbations for blocking adversarial attacks in text classification"):
  with members:
    - "robust semantic parsing with adversarial learning for domain generalization"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "deep text classification can be fooled"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "generating natural language adversarial examples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "deflecting adversarial attacks with pixel deflection"

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)