human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "text processing like humans do visually attacking and shielding nlp systems"):
  Abstract: Visual modifications to text are often used to obfuscate offensive comments in social media (e.g., "!d10t") or as a writing style ("1337" in "leet speak"), among other scenarios. We consider this as a new type of adversarial attack in NLP, a setting to which humans are very robust, as our experiments with both simple and more difficult visual input perturbations demonstrate. We then investigate the impact of visual adversarial attacks on current NLP systems on character-, word-, and sentence-level tasks, showing that both neural and non-neural models are, in contrast to humans, extremely sensitive to such attacks, suffering performance decreases of up to 82\%. We then explore three shielding methods---visual character embeddings, adversarial training, and rule-based recovery---which substantially improve the robustness of the models. However, the shielding methods still fall behind performances achieved in non-attack scenarios, which demonstrates the difficulty of dealing with visual attacks.

Includes members:
  Member "is it time to swish comparing deep learning activation functions across nlp tasks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effect of activation functions on the training of overparametrized neural nets")
  Member "adversarial example generation with syntactically controlled paraphrase networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "strong baselines for neural semi supervised learning under domain shift":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pseudo labeling and negative feedback learning for large scale multi label domain classification")
  Member "generating natural language adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "shielding google s language toxicity model against adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "natural language adversarial attacks and defenses in word level")
  Member "cross lingual argumentation mining machine translation and a bit of projection is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a discriminative neural model for cross lingual word alignment")
  Member "deceiving google s perspective api built for detecting toxic comments":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection")

Question: Please tell me which class the source paper 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)