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 "mitigating deep learning vulnerabilities from adversarial examples attack in the cybersecurity domain"):
  Abstract: Deep learning models are known to solve classification and regression problems by employing a number of epoch and training samples on a large dataset with optimal accuracy. However, that doesn't mean they are attack-proof or unexposed to vulnerabilities. Newly deployed systems particularly on a public environment (i.e public networks) are vulnerable to attacks from various entities. Moreover, published research on deep learning systems (Goodfellow et al., 2014) have determined a significant number of attacks points and a wide array of attack surface that has evidence of exploitation from adversarial examples. Successful exploit on these systems could lead to critical real world repercussions. For instance, (1) an adversarial attack on a self-driving car running a deep reinforcement learning system yields a direct misclassification on humans causing untoward accidents.(2) a self-driving vehicle misreading a red light signal may cause the car to crash to another car (3) misclassification of a pedestrian lane as an intersection lane that could lead to car crashes. This is just the tip of the iceberg, computer vision deployment are not entirely focused on self-driving cars but on many other areas as well - that would have definitive impact on the real-world. These vulnerabilities must be mitigated at an early stage of development. It is imperative to develop and implement baseline security standards at a global level prior to real-world deployment.

Includes members:
  Member "mitigating adversarial effects through randomization":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")
  Member "adversarial examples attacks and defenses for deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "varmixup exploiting the latent space for robust training and inference")
  Member "adversarial machine learning at scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial ranking attack and defense")

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)