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 "random directional attack for fooling deep neural networks"):
  Abstract: Deep neural networks (DNNs) have been widely used in many fields such as images processing, speech recognition; however, they are vulnerable to adversarial examples, and this is a security issue worthy of attention. Because the training process of DNNs converge the loss by updating the weights along the gradient descent direction, many gradient-based methods attempt to destroy the DNN model by adding perturbations in the gradient direction. Unfortunately, as the model is nonlinear in most cases, the addition of perturbations in the gradient direction does not necessarily increase loss. Thus, we propose a random directed attack (RDA) for generating adversarial examples in this paper. Rather than limiting the gradient direction to generate an attack, RDA searches the attack direction based on hill climbing and uses multiple strategies to avoid local optima that cause attack failure. Compared with state-of-the-art gradient-based methods, the attack performance of RDA is very competitive. Moreover, RDA can attack without any internal knowledge of the model, and its performance under black-box attack is similar to that of the white-box attack in most cases, which is difficult to achieve using existing gradient-based attack methods.

Includes members:
  Member "one pixel attack for fooling deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "adversarial ranking attack and defense")
  Member "detecting adversarial image examples in deep neural networks with adaptive noise reduction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non intrusive detection of adversarial deep learning attacks via observer networks")
  Member "semantic adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "breaking certified defenses semantic adversarial examples with spoofed robustness certificates")
  Member "disentangling adversarial robustness and generalization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "adversarial perturbations prevail in the y channel of the ycbcr color space")
  Member "did you hear that adversarial examples against automatic speech recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards query efficient black box adversary with zeroth order natural gradient descent")
  Member "characterizing adversarial subspaces using local intrinsic dimensionality":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "defense gan protecting classifiers against adversarial attacks using generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "comdefend an efficient image compression model to defend adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to structure an image with few colors")

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.CR(Cryptography and Security)