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 "n ml mitigating adversarial examples via ensembles of topologically manipulated classifiers"):
  Abstract: This paper proposes a new defense called $n$-ML against adversarial examples, i.e., inputs crafted by perturbing benign inputs by small amounts to induce misclassifications by classifiers. Inspired by $n$-version programming, $n$-ML trains an ensemble of $n$ classifiers, and inputs are classified by a vote of the classifiers in the ensemble. Unlike prior such approaches, however, the classifiers in the ensemble are trained specifically to classify adversarial examples differently, rendering it very difficult for an adversarial example to obtain enough votes to be misclassified. We show that $n$-ML roughly retains the benign classification accuracies of state-of-the-art models on the MNIST, CIFAR10, and GTSRB datasets, while simultaneously defending against adversarial examples with better resilience than the best defenses known to date and, in most cases, with lower classification-time overhead.

Includes members:
  Member "should adversarial attacks use pixel p norm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "generating semantic adversarial examples with differentiable rendering")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "improving adversarial robustness via promoting ensemble diversity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "adversarial training for free":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "evading defenses to transferable adversarial examples by translation invariant attacks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "adversarial ranking attack and defense")
  Member "adversarial sample detection for deep neural network through model mutation testing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reliability validation of learning enabled vehicle tracking")
  Member "protecting neural networks with hierarchical random switching towards better robustness accuracy trade off for stochastic defenses":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "rtmobile beyond real time mobile acceleration of rnns for speech recognition")
  Member "a convex relaxation barrier to tight robustness verification of neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "overfitting in adversarially robust deep learning")

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)