human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "adversarial attacks on cognitive self organizing networks the challenge and the way forward"
  Abstract: Future communications and data networks are expected to be largely cognitive self-organizing networks (CSON). Such networks will have the essential property of cognitive self-organization, which can be achieved using machine learning techniques (e.g., deep learning). Despite the potential of these techniques, these techniques in their current form are vulnerable to adversarial attacks that can cause cascaded damages with detrimental consequences for the whole network. In this paper, we explore the effect of adversarial attacks on CSON. Our experiments highlight the level of threat that CSON have to deal with in order to meet the challenges of next-generation networks and point out promising directions for future work.

Connected through hyperedge HE-1 (induced by source paper "the adversarial machine learning conundrum can the insecurity of ml become the achilles heel of cognitive networks"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "why and how networks should run themselves"
    - "towards evaluating the robustness of neural networks"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "the limitations of deep learning in adversarial settings"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-2 (induced by source paper "adversarial ml attack on self organizing cellular networks"):
  with members:
    - "explainable failure predictions with rnn classifiers based on time series data"
    - "an adversarial approach for explainable ai in intrusion detection systems"
    - "practical black box attacks against machine learning"
    - "distillation as a defense to adversarial perturbations against deep neural networks"
    - "intriguing properties of neural networks"

Question: Please tell me which class the center node 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)