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 "certified robustness to adversarial word substitutions"):
  Abstract: State-of-the-art NLP models can often be fooled by adversaries that apply seemingly innocuous label-preserving transformations (e.g., paraphrasing) to input text. The number of possible transformations scales exponentially with text length, so data augmentation cannot cover all transformations of an input. This paper considers one exponentially large family of label-preserving transformations, in which every word in the input can be replaced with a similar word. We train the first models that are provably robust to all word substitutions in this family. Our training procedure uses Interval Bound Propagation (IBP) to minimize an upper bound on the worst-case loss that any combination of word substitutions can induce. To evaluate models' robustness to these transformations, we measure accuracy on adversarially chosen word substitutions applied to test examples. Our IBP-trained models attain $75\%$ adversarial accuracy on both sentiment analysis on IMDB and natural language inference on SNLI. In comparison, on IMDB, models trained normally and ones trained with data augmentation achieve adversarial accuracy of only $8\%$ and $35\%$, respectively.

Includes members:
  Member "inoculation by fine tuning a method for analyzing challenge datasets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial filters of dataset biases")
  Member "combating adversarial misspellings with robust word recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stance detection benchmark how robust is your stance detection")
  Member "on the effectiveness of interval bound propagation for training verifiably robust models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "adversarial example generation with syntactically controlled paraphrase networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stress test evaluation of transformer based models in natural language understanding tasks")
  Member "training verified learners with learned verifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "black box smoothing a provable defense for pretrained classifiers")
  Member "generating natural language adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "certified defenses against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    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.CL(Computation and Language)