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 examples for deep learning cyber security analytics"
  Abstract: As advances in Deep Neural Networks demonstrate unprecedented levels of performance in many critical applications, their vulnerability to attacks is still an open question. Adversarial examples are small modifications of legitimate data points, resulting in mis-classification at testing time. As Deep Neural Networks found a wide range of applications to cyber security analytics, it becomes important to study the robustness of these models in this setting. We consider adversarial testing-time attacks against Deep Learning models designed for cyber security applications. In security applications, machine learning models are not typically trained directly on the raw network traffic or security logs, but on intermediate features defined by domain experts. Existing attacks applied directly to the intermediate feature representation result in violation of feature constraints, leading to invalid adversarial examples. We propose a general framework for crafting adversarial attacks that takes into consideration the mathematical dependencies between intermediate features in model input vector, as well as physical constraints imposed by the applications. We apply our methods on two security applications, a malicious connection and a malicious domain classifier, to generate feasible adversarial examples in these domains. We show that with minimal effort (e.g., generating 12 network connections), an attacker can change the prediction of a model from Malicious to Benign. We extensively evaluate the success of our attacks, and how they depend on several optimization objectives and imbalance ratios in the training data.

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)