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 "packetcgan exploratory study of class imbalance for encrypted traffic classification using cgan"):
  Abstract: With more and more adoption of Deep Learning (DL) in the field of image processing, computer vision and NLP, researchers have begun to apply DL to tackle with encrypted traffic classification problems. Although these methods can automatically extract traffic features to overcome the difficulty of traditional classification methods like DPI in terms of feature engineering, a large amount of data is needed to learn the characteristics of various types of traffic. Therefore, the performance of classification model always significantly depends on the quality of datasets. Nevertheless, the building of datasets is a time-consuming and costly task, especially encrypted traffic data. Apparently, it is often more difficult to collect a large amount of traffic samples of those unpopular encrypted applications than well-known, which leads to the problem of class imbalance between major and minor encrypted applications in datasets. In this paper, we proposed a novel traffic data augmenting method called PacketCGAN using Conditional GAN. As a generative model, PacketCGAN exploit the benefit of CGAN to generate specified traffic to address the problem of the datasets' imbalance. As a proof of concept, three classical DL models like Convolutional Neural Network (CNN) were adopted and designed to classify four encrypted traffic datasets augmented by Random Over Sampling (ROS), SMOTE(Synthetic Minority Over-sampling Techinique) , vanilla GAN and PacketCGAN respectively based on two public datasets: ISCX2012 and USTC-TFC2016. The experimental evaluation results demonstrate that DL based encrypted traffic classifier over dataset augmented by PacketCGAN can achieve better performance than the others.

Includes members:
  Member "augmentation scheme for dealing with imbalanced network traffic classification using deep learning":
    also connected through other hyperedges:
      (none)
  Member "a smart home gateway platform for data collection and awareness":
    also connected through other hyperedges:
      (none)
  Member "anomaly generation using generative adversarial networks in host based intrusion detection":
    also connected through other hyperedges:
      (none)
  Member "idsgan generative adversarial networks for attack generation against intrusion detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the threat of adversarial attacks on machine learning in network security a survey")
  Member "a hierarchical approach to encrypted data packet classification in smart home gateways":
    also connected through other hyperedges:
      (none)

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)