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: "deceiving google s perspective api built for detecting toxic comments"
  Abstract: Social media platforms provide an environment where people can freely engage in discussions. Unfortunately, they also enable several problems, such as online harassment. Recently, Google and Jigsaw started a project called Perspective, which uses machine learning to automatically detect toxic language. A demonstration website has been also launched, which allows anyone to type a phrase in the interface and instantaneously see the toxicity score [1]. In this paper, we propose an attack on the Perspective toxic detection system based on the adversarial examples. We show that an adversary can subtly modify a highly toxic phrase in a way that the system assigns significantly lower toxicity score to it. We apply the attack on the sample phrases provided in the Perspective website and show that we can consistently reduce the toxicity scores to the level of the non-toxic phrases. The existence of such adversarial examples is very harmful for toxic detection systems and seriously undermines their usability.

Connected through hyperedge HE-1 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection"):
  with members:
    - "conversational networks for automatic online moderation"
    - "trouble on the horizon forecasting the derailment of online conversations as they develop"
    - "abusive language detection in online conversations by combining content and graph based features"
    - "all you need is love evading hate speech detection"
    - "neural character based composition models for abuse detection"
    - "wikiconv a corpus of the complete conversational history of a large online collaborative community"
    - "deep learning for user comment moderation"
    - "ex machina personal attacks seen at scale"

Connected through hyperedge HE-2 (induced by source paper "from pick up artists to incels a data driven sketch of the manosphere"):
  with members:
    - "what is gab a bastion of free speech or an alt right echo chamber"
    - "the effect of extremist violence on hateful speech online"
    - "analyzing right wing youtube channels hate violence and discrimination"
    - "on the origins of memes by means of fringe web communities"
    - "characterizing and detecting hateful users on twitter"
    - "automated hate speech detection and the problem of offensive language"
    - "kek cucks and god emperor trump a measurement study of 4chan s politically incorrect forum and its effects on the web"

Connected through hyperedge HE-3 (induced by source paper "on evaluation of adversarial perturbations for sequence to sequence models"):
  with members:
    - "on adversarial examples for character level neural machine translation"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "generating natural language adversarial examples"
    - "seq2sick evaluating the robustness of sequence to sequence models with adversarial examples"
    - "a call for clarity in reporting bleu scores"
    - "stress test evaluation for natural language inference"
    - "towards crafting text adversarial samples"
    - "provable defenses against adversarial examples via the convex outer adversarial polytope"

Connected through hyperedge HE-4 (induced by source paper "text processing like humans do visually attacking and shielding nlp systems"):
  with members:
    - "is it time to swish comparing deep learning activation functions across nlp tasks"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "strong baselines for neural semi supervised learning under domain shift"
    - "generating natural language adversarial examples"
    - "deep contextualized word representations"
    - "shielding google s language toxicity model against adversarial attacks"
    - "cross lingual argumentation mining machine translation and a bit of projection is all you need"
    - "attacking visual language grounding with adversarial examples a case study on neural image captioning"

Connected through hyperedge HE-5 (induced by source paper "towards robust toxic content classification"):
  with members:
    - "on adversarial examples for character level neural machine translation"
    - "challenges for toxic comment classification an in depth error analysis"
    - "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"
    - "deep contextualized word representations"
    - "neural character based composition models for abuse detection"
    - "towards crafting text adversarial samples"

Connected through hyperedge HE-6 (induced by source paper "white to black efficient distillation of black box adversarial attacks"):
  with members:
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "black box generation of adversarial text sequences to evade deep learning classifiers"
    - "shielding google s language toxicity model against adversarial attacks"
    - "the fine line between linguistic generalization and failure in seq2seq attention models"
    - "synthetic and natural noise both break neural machine translation"
    - "adversarial examples for evaluating reading comprehension systems"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "delving into transferable adversarial examples and black box attacks"

Connected through hyperedge HE-7 (induced by source paper "build it break it fix it for dialogue safety robustness from adversarial human attack"):
  with members:
    - "the second conversational intelligence challenge convai2"
    - "semeval 2019 task 6 identifying and categorizing offensive language in social media offenseval"
    - "all you need is love evading hate speech detection"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "automated hate speech detection and the problem of offensive language"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "ethical challenges in data driven dialogue systems"

Connected through hyperedge HE-8 (induced by source paper "conversational networks for automatic online moderation"):
  with members:
    - "impact of content features for automatic online abuse detection"
    - "one step and two step classification for abusive language detection on twitter"
    - "graph based features for automatic online abuse detection"
    - "deep learning for user comment moderation"
    - "a community role approach to assess social capitalists visibility in the twitter network"
    - "antisocial behavior in online discussion communities"
    - "quantifying controversy in social media"
    - "identifying the community roles of social capitalists in the twitter network"

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)