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 "towards resilient machine learning for ransomware detection"):
  Abstract: There has been a surge of interest in using machine learning (ML) to automatically detect malware through their dynamic behaviors. These approaches have achieved significant improvement in detection rates and lower false positive rates at large scale compared with traditional malware analysis methods. ML in threat detection has demonstrated to be a good cop to guard platform security. However it is imperative to evaluate - is ML-powered security resilient enough? #R##N#In this paper, we juxtapose the resiliency and trustworthiness of ML algorithms for security, via a case study of evaluating the resiliency of ransomware detection via the generative adversarial network (GAN). In this case study, we propose to use GAN to automatically produce dynamic features that exhibit generalized malicious behaviors that can reduce the efficacy of black-box ransomware classifiers. We examine the quality of the GAN-generated samples by comparing the statistical similarity of these samples to real ransomware and benign software. Further we investigate the latent subspace where the GAN-generated samples lie and explore reasons why such samples cause a certain class of ransomware classifiers to degrade in performance. Our focus is to emphasize necessary defense improvement in ML-based approaches for ransomware detection before deployment in the wild. Our results and discoveries should pose relevant questions for defenders such as how ML models can be made more resilient for robust enforcement of security objectives.

Includes members:
  Member "deep learning for malicious flow detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recover and identify a generative dual model for cross resolution person re identification")
  Member "wild patterns ten years after the rise of adversarial machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "vulnerabilities of connectionist ai applications evaluation and defence")
  Member "detecting adversarial examples via neural fingerprinting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "metaadvdet towards robust detection of evolving adversarial attacks")
  Member "leveraging machine learning techniques for windows ransomware network traffic detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cyber threat intelligence challenges and opportunities")
  Member "adversarial logit pairing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast is better than free revisiting adversarial training")
  Member "deep transfer learning for static malware classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "random capsnet forest model for imbalanced malware type classification task")
  Member "generating adversarial examples with adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "category wise attack transferable adversarial examples for anchor free object detection")
  Member "black box adversarial attacks with limited queries and information":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "universal adversarial attack on attention and the resulting dataset damagenet")

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.LG(Machine Learning)