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 "charbot a simple and effective method for evading dga classifiers"):
  Abstract: Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names which can be used for command and control (C&C) purposes. Approaches based on machine learning have recently been developed to automatically detect generated domain names in real-time. In this work, we present a novel DGA called CharBot which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM.MI (a deep learning approach). CharBot is very simple, effective and requires no knowledge of the targeted DGA classifiers. We show that retraining the classifiers on CharBot samples is not a viable defense strategy. We believe these findings show that DGA classifiers are inherently vulnerable to adversarial attacks if they rely only on the domain name string to make a decision. Designing a robust DGA classifier may, therefore, necessitate the use of additional information besides the domain name alone. To the best of our knowledge, CharBot is the simplest and most efficient black-box adversarial attack against DGA classifiers proposed to date.

Includes members:
  Member "maskdga a black box evasion technique against dga classifiers and adversarial defenses":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inline detection of dga domains using side information")
  Member "inline detection of domain generation algorithms with context sensitive word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "inline detection of dga domains using side information")
  Member "semidefinite relaxations for certifying robustness to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "overfitting in adversarially robust deep learning")
  Member "adversarial texts with gradient methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generalizable adversarial attacks with latent variable perturbation modelling")
  Member "passgan a deep learning approach for password guessing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving password guessing via representation learning")
  Member "expose a character level convolutional neural network with embeddings for detecting malicious urls file paths and registry keys":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nested multiple instance learning in modelling of http network traffic")
  Member "generating adversarial malware examples for black box attacks based on gan":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "automatic generation of adversarial examples for interpreting malware classifiers")
  Member "automatic detection of malware generated domains with recurrent neural models":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "exploiting statistical and structural features for the detection of domain generation algorithms")

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)