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 "l2 nonexpansive neural networks"):
  Abstract: This paper proposes a class of well-conditioned neural networks in which a unit amount of change in the inputs causes at most a unit amount of change in the outputs or any of the internal layers. We develop the known methodology of controlling Lipschitz constants to realize its full potential in maximizing robustness: our linear and convolution layers subsume those in the previous Parseval networks as a special case and allow greater degrees of freedom; aggregation, pooling, splitting and other operators are adapted in new ways, and a new loss function is proposed, all for the purpose of improving robustness. With MNIST and CIFAR-10 classifiers, we demonstrate a number of advantages. Without needing any adversarial training, the proposed classifiers exceed the state of the art in robustness against white-box L2-bounded adversarial attacks. Their outputs are quantitatively more meaningful than ordinary networks and indicate levels of confidence. They are also free of exploding gradients, among other desirable properties.

Includes members:
  Member "scaling provable adversarial defenses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "did you hear that adversarial examples against automatic speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent")
  Member "black box generation of adversarial text sequences to evade deep learning classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "stable architectures for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a comprehensive and modularized statistical framework for gradient norm equality in deep neural networks")
  Member "audio adversarial examples targeted attacks on speech to text":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "certified defenses against adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (induced by source paper "adversarial ranking attack and defense")
  Member "threat of adversarial attacks on deep learning in computer vision a survey":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement 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.AI(Artificial Intelligence)