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 "white to black efficient distillation of black box adversarial attacks"):
  Abstract: Adversarial examples are important for understanding the behavior of neural models, and can improve their robustness through adversarial training. Recent work in natural language processing generated adversarial examples by assuming white-box access to the attacked model, and optimizing the input directly against it (Ebrahimi et al., 2018). In this work, we show that the knowledge implicit in the optimization procedure can be distilled into another more efficient neural network. We train a model to emulate the behavior of a white-box attack and show that it generalizes well across examples. Moreover, it reduces adversarial example generation time by 19x-39x. We also show that our approach transfers to a black-box setting, by attacking The Google Perspective API and exposing its vulnerability. Our attack flips the API-predicted label in 42\% of the generated examples, while humans maintain high-accuracy in predicting the gold label.

Includes members:
  Member "adversarial example generation with syntactically controlled paraphrase networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "black box generation of adversarial text sequences to evade deep learning classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "shielding google s language toxicity model against adversarial attacks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "text processing like humans do visually attacking and shielding nlp systems")
  Member "the fine line between linguistic generalization and failure in seq2seq attention models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "right for the wrong reasons diagnosing syntactic heuristics in natural language inference")
  Member "deceiving google s perspective api built for detecting toxic comments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "wac a corpus of wikipedia conversations for online abuse detection")
  Member "synthetic and natural noise both break neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "adversarial examples for evaluating reading comprehension systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "transferability in machine learning from phenomena to black box attacks using adversarial samples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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.LG(Machine Learning)