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 "universal adversarial triggers for attacking and analyzing nlp"):
  Abstract: Adversarial examples highlight model vulnerabilities and are useful for evaluation and interpretation. We define universal adversarial triggers: input-agnostic sequences of tokens that trigger a model to produce a specific prediction when concatenated to any input from a dataset. We propose a gradient-guided search over tokens which finds short trigger sequences (e.g., one word for classification and four words for language modeling) that successfully trigger the target prediction. For example, triggers cause SNLI entailment accuracy to drop from 89.94% to 0.55%, 72% of "why" questions in SQuAD to be answered "to kill american people", and the GPT-2 language model to spew racist output even when conditioned on non-racial contexts. Furthermore, although the triggers are optimized using white-box access to a specific model, they transfer to other models for all tasks we consider. Finally, since triggers are input-agnostic, they provide an analysis of global model behavior. For instance, they confirm that SNLI models exploit dataset biases and help to diagnose heuristics learned by reading comprehension models.

Includes members:
  Member "right for the wrong reasons diagnosing syntactic heuristics in natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases")
  Member "on evaluation of adversarial perturbations for sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adv bert bert is not robust on misspellings generating nature adversarial samples on bert")
  Member "adversarial attacks on deep learning models in natural language processing a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "gradient based adversarial attacks on categorical sequence models via traversing an embedded world")
  Member "on adversarial examples for character level neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "linguistic generalization and compositionality in modern artificial neural networks")
  Member "adversarial example generation with syntactically controlled paraphrase networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "annotation artifacts in natural language inference data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial filters of dataset biases")
  Member "adversarial patch":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "what makes reading comprehension questions easier":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")

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)