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 "evolving robust neural architectures to defend from adversarial attacks"):
  Abstract: Deep neural networks were shown to misclassify slightly modified input images. Recently, many defenses have been proposed but none have improved consistently the robustness of neural networks. Here, we propose to use attacks as a function evaluation to automatically search for architectures that can resist such attacks. Experiments on neural architecture search algorithms from the literature show that although their accurate results, they are not able to find robust architectures. Most of the reason for this lies in their limited search space. By creating a novel neural architecture search with options for dense layers to connect with convolution layers and vice-versa as well as the addition of multiplication, addition and concatenation layers in the search space, we were able to evolve an architecture that is $58\%$ accurate on adversarial samples. Interestingly, this inherent robustness of the evolved architecture rivals state-of-the-art defenses such as adversarial training while being trained only on the training dataset. Moreover, the evolved architecture makes use of some peculiar traits which might be useful for developing even more robust ones. Thus, the results here demonstrate that more robust architectures exist as well as opens up a new range of possibilities for the development and exploration of deep neural networks using automatic architecture search. #R##N#Code available at this http URL.

Includes members:
  Member "evolving deep neural networks by multi objective particle swarm optimization for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "particle swarm optimisation for evolving deep neural networks for image classification by evolving and stacking transferable blocks")
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial ranking attack and defense")
  Member "evolutionary neural automl for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neuroevolution of neural network architectures using codeepneat and keras")
  Member "understanding the one pixel attack propagation maps and locality analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "uncovering why deep neural networks lack robustness representation metrics that link to adversarial attacks")
  Member "model agnostic dual quality assessment for adversarial machine learning and an analysis of current neural networks and defenses":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "uncovering why deep neural networks lack robustness representation metrics that link to adversarial attacks")
  Member "adversarial patch":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "sentinet detecting localized universal attacks against deep learning systems")
  Member "evolutionary architecture search for deep multitask networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deeper insights into weight sharing in neural architecture search")
  Member "characterizing adversarial subspaces using local intrinsic dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "anomalous instance detection in deep learning a survey")

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)