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 "inline detection of dga domains using side information"):
  Abstract: Malware applications typically use a command and control (C&C) server to manage bots to perform malicious activities. Domain Generation Algorithms (DGAs) are popular methods for generating pseudo-random domain names that can be used to establish a communication between an infected bot and the C&C server. In recent years, machine learning based systems have been widely used to detect DGAs. There are several well known state-of-the-art classifiers in the literature that can detect DGA domain names in real-time applications with high predictive performance. However, these DGA classifiers are highly vulnerable to adversarial attacks in which adversaries purposely craft domain names to evade DGA detection classifiers. In our work, we focus on hardening DGA classifiers against adversarial attacks. To this end, we train and evaluate state-of-the-art deep learning and random forest (RF) classifiers for DGA detection using side information that is harder for adversaries to manipulate than the domain name itself. Additionally, the side information features are selected such that they are easily obtainable in practice to perform inline DGA detection. The performance and robustness of these models is assessed by exposing them to one day of real-traffic data as well as domains generated by adversarial attack algorithms. We found that the DGA classifiers that rely on both the domain name and side information have high performance and are more robust against adversaries.

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 "charbot a simple and effective method for evading dga classifiers")
  Member "using lexical features for malicious url detection a machine learning approach":
    also connected through other hyperedges:
      (none)
  Member "charbot a simple and effective method for evading dga classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "domaingan generating adversarial examples to attack domain generation algorithm classifiers")
  Member "detecting dga domains with recurrent neural networks and side information":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "encrypted and covert dns queries for botnets challenges and countermeasures")
  Member "inline detection of domain generation algorithms with context sensitive word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "maskdga a black box evasion technique against dga classifiers and adversarial defenses")
  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-5 (induced by source paper "nested multiple instance learning in modelling of http network traffic")
  Member "predicting domain generation algorithms with long short term memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nested multiple instance learning in modelling of http network traffic")
  Member "deepdga adversarially tuned domain generation and detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "encrypted and covert dns queries for botnets challenges and countermeasures")

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)