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 "active anomaly detection via ensembles insights algorithms and interpretability"):
  Abstract: Anomaly detection (AD) task corresponds to identifying the true anomalies from a given set of data instances. AD algorithms score the data instances and produce a ranked list of candidate anomalies, which are then analyzed by a human to discover the true anomalies. However, this process can be laborious for the human analyst when the number of false-positives is very high. Therefore, in many real-world AD applications including computer security and fraud prevention, the anomaly detector must be configurable by the human analyst to minimize the effort on false positives. #R##N#In this paper, we study the problem of active learning to automatically tune ensemble of anomaly detectors to maximize the number of true anomalies discovered. We make four main contributions towards this goal. First, we present an important insight that explains the practical successes of AD ensembles and how ensembles are naturally suited for active learning. Second, we present several algorithms for active learning with tree-based AD ensembles. These algorithms help us to improve the diversity of discovered anomalies, generate rule sets for improved interpretability of anomalous instances, and adapt to streaming data settings in a principled manner. Third, we present a novel algorithm called GLocalized Anomaly Detection (GLAD) for active learning with generic AD ensembles. GLAD allows end-users to retain the use of simple and understandable global anomaly detectors by automatically learning their local relevance to specific data instances using label feedback. Fourth, we present extensive experiments to evaluate our insights and algorithms. Our results show that in addition to discovering significantly more anomalies than state-of-the-art unsupervised baselines, our active learning algorithms under the streaming-data setup are competitive with the batch setup.

Includes members:
  Member "active anomaly detection via ensembles":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "should i raise the red flag a comprehensive survey of anomaly scoring methods toward mitigating false alarms")
  Member "revisiting perceptron efficient and label optimal learning of halfspaces":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")
  Member "explaining anomalies in groups with characterizing subspace rules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "detection of thin boundaries between different types of anomalies in outlier detection using enhanced neural networks")
  Member "why should i trust you explaining the predictions of any classifier":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "toward supervised anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep weakly supervised anomaly detection")
  Member "statistical active learning algorithms for noise tolerance and differential privacy":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "locally private learning without interaction requires separation")

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)