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 boost strategy to the generative error based video anomaly detection algorithms"):
  Abstract: The generation error (GE) based algorithms show excellent performances in the task of video anomaly detection. However, in the step of anomaly detection, they have two problems: (1) Abnormal events usually occur in local areas. It reduces the saliencies of the abnormal events to use the frame-level GE as the anomaly-score. (2) Every discrimination has both advantages and disadvantages, it is difficult to aggregate multiple discriminations effectively. To address these problems, we propose a promotion strategy which is consisted of two modules. Firstly, we replace the frame-level GE with the maximum of the block-level GEs in a frame as the anomaly score. Secondly, assuming that the stricter the anomaly threshold the more reliable the anomaly detected, we propose a reliable-anomaly (R-anomaly) based multiple discriminations aggregation method. In this method, we set a strict anomaly detection threshold (SADT) for each auxiliary discrimination to detect R-anomalies. Then we use the detected R-anomalies to enhance their anomaly scores in the main discrimination. Experiments are carried out on UCSD and CUHK Avenue datasets. The results demonstrate the effectiveness of the proposed strategy and achieve state-of-the-art performance.

Includes members:
  Member "video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a promotion method for generation error based video anomaly detection")
  Member "detecting abnormal events in video using narrowed normality clusters":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "joint detection and recounting of abnormal events by learning deep generic knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "abnormal event detection in videos using spatiotemporal autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "anomaly detection in video using predictive convolutional long short term memory networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "anomaly detection in road traffic using visual surveillance a survey")

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.CV(Computer Vision and Pattern Recognition)