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 "an adaptive system for detecting malicious queries in web attacks"):
  Abstract: Web request query strings (queries), which pass parameters to a referenced resource, are always manipulated by attackers to retrieve sensitive data and even take full control of victim web servers and web applications. However, existing malicious query detection approaches in the literature cannot cope with changing web attacks. In this paper, we introduce a novel adaptive system (AMOD) that can adaptively detect web-based code injection attacks, which are the majority of web attacks, by analyzing queries. We also present a new adaptive learning strategy, called SVM HYBRID, leveraged by our system to minimize manual work. In the evaluation, an up-to-date detection model is trained on a ten-day query dataset collected from an academic institute’s web server logs. The evaluation shows our approach overwhelms existing approaches in two respects. Firstly, AMOD outperforms existing web attack detection methods with an F-value of 99.50% and FP rate of 0.001%. Secondly, the total number of malicious queries obtained by SVM HYBRID is 3.07 times that by the popular support vector machine adaptive learning (SVM AL) method. The malicious queries obtained can be used to update the web application firewall (WAF) signature library.

Includes members:
  Member "active distance based clustering using k medoids":
    also connected through other hyperedges:
      (none)
  Member "active clustering with model based uncertainty reduction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "leveraging union of subspace structure to improve constrained clustering")
  Member "machine learning in automated text categorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "utilizing neural networks and linguistic metadata for early detection of depression indications in text sequences")

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)