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 "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.

Includes members:
  Member "robust audio adversarial example for a physical attack":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "advbox a toolbox to generate adversarial examples that fool neural networks")
  Member "certified adversarial robustness via randomized smoothing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "on designing machine learning models for malicious network traffic classification":
    also connected through other hyperedges:
      (none)
  Member "rallying adversarial techniques against deep learning for network security":
    also connected through other hyperedges:
      (none)
  Member "evading classifiers in discrete domains with provable optimality guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "provably robust boosted decision stumps and trees against adversarial attacks")
  Member "kitsune an ensemble of autoencoders for online network intrusion detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "text a 3 activation anomaly analysis")
  Member "black box generation of adversarial text sequences to evade deep learning classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "humpty dumpty controlling word meanings via corpus poisoning")
  Member "deceiving end to end deep learning malware detectors using adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")

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)