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 "a content based deep intrusion detection system"):
  Abstract: By growing the number of Internet users and the prevalence of web applications, we have to deal with very complex software and applications in the network. This results in an increasing number of new vulnerabilities in the systems, which consequently leads to an increase in the cyber and, in particular, zero-day attacks. The cost of generating appropriate signatures for these attacks is a potential motive for using machine learning-based methodologies. Although there exist many studies on the use of learning-based methods for attack detection, they generally use extracted features and overlook raw contents. This approach can lessen the performance of detection systems against content-based attacks like SQL injection, Cross-site Scripting (XSS), and various viruses. #R##N#As a new paradigm, in this work, we propose a scheme, called deep intrusion detection (DID) system that uses the pure content of traffic flows in addition to traffic metadata in the learning and detection phases. To this end, we employ deep learning techniques recently developed in the machine learning community. Due to the inherent nature of deep learning, it can process high dimensional data content and, accordingly, discover the sophisticated relations between the auto extracted features of the traffic. To evaluate the proposed DID system, we use the ISCX IDS 2017 dataset. The evaluation metrics, such as precision and recall, reach $0.992$ and $0.998$, respectively, which show the high performance of the proposed DID method.

Includes members:
  Member "a tutorial on learning with bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a comprehensive scoping review of bayesian networks in healthcare past present and future")
  Member "a neural network architecture combining gated recurrent unit gru and support vector machine svm for intrusion detection in network traffic data":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nestedvae isolating common factors via weak supervision")

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.NI(Networking and Internet Architecture)