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 "do imagenet classifiers generalize to imagenet"):
  Abstract: We build new test sets for the CIFAR-10 and ImageNet datasets. Both benchmarks have been the focus of intense research for almost a decade, raising the danger of overfitting to excessively re-used test sets. By closely following the original dataset creation processes, we test to what extent current classification models generalize to new data. We evaluate a broad range of models and find accuracy drops of 3% - 15% on CIFAR-10 and 11% - 14% on ImageNet. However, accuracy gains on the original test sets translate to larger gains on the new test sets. Our results suggest that the accuracy drops are not caused by adaptivity, but by the models' inability to generalize to slightly "harder" images than those found in the original test sets.

Includes members:
  Member "benchmarking neural network robustness to common corruptions and perturbations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "wild patterns ten years after the rise of adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "do better imagenet models transfer better":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a large scale study of representation learning with the visual task adaptation benchmark")
  Member "spatially transformed adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial ranking attack and defense")
  Member "semantic adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates")
  Member "autoaugment learning augmentation policies from data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "intelligence physics and information the tradeoff between accuracy and simplicity in machine learning")
  Member "generalisation in humans and deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an empirical evaluation on robustness and uncertainty of regularization methods")
  Member "progressive neural architecture search":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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.CV(Computer Vision and Pattern Recognition)