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 "systematic construction of anomaly detection benchmarks from real data"):
  Abstract: Research in anomaly detection suffers from a lack of realistic and publicly-available data sets. Because of this, most published experiments in anomaly detection validate their algorithms with application-specific case studies or benchmark datasets of the researchers' construction. This makes it difficult to compare different methods or to measure progress in the field. It also limits our ability to understand the factors that determine the performance of anomaly detection algorithms. This article proposes a new methodology for empirical analysis and evaluation of anomaly detection algorithms. It is based on generating thousands of benchmark datasets by transforming existing supervised learning benchmark datasets and manipulating properties relevant to anomaly detection. The paper identifies and validates four important dimensions: (a) point difficulty, (b) relative frequency of anomalies, (c) clusteredness of anomalies, and (d) relevance of features. We apply our generated datasets to analyze several leading anomaly detection algorithms. The evaluation verifies the importance of these dimensions and shows that, while some algorithms are clearly superior to others, anomaly detection accuracy is determined more by variation in the four dimensions than by the choice of algorithm.

Includes members:
  Member "dendritic cells for syn scan detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "integrating real time analysis with the dendritic cell algorithm through segmentation")
  Member "dendritic cells for anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probabilistic modeling for novelty detection with applications to fraud identification")

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.AI(Artificial Intelligence)