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 promotion method for generation error based video anomaly detection"):
  Abstract: Surveillance video anomaly detection is to detect events that rarely or never happened in a certain scene. The generation error (GE)-based methods exhibit excellent performance on this task. They firstly train a generative neural network (GNN) to generate normal samples, then judge the samples with large GEs as anomalies. Almost all the GE-based methods utilize frame-level GEs to detect anomalies. However, anomalies generally occur in local areas, the frame-level GE introduces GEs of normal areas to anomaly discriminations, that brings two problems: i) The GE of normal areas reduces the anomaly saliency of the anomalous frame. ii) Different videos have different normal-GE-levels, thus it is hard to set a uniform threshold for all videos to detect anomalies. To address these problems, we propose a promotion method: utilize the maximum of block-level GEs on the frame to detect anomaly. Firstly, we calculate the block-level GEs at each position on the frame. Then, we utilize the maximum of the block-level GEs on the frame to detect anomalies. Based on the existed GNN models, experiments are carried out on multiple datasets. The results demonstrate the effectiveness of the proposed method and achieve state-of-the-art performance.

Includes members:
  Member "deep learning for anomaly detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "strategy to increase the safety of a dnn based perception for had systems")
  Member "video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "distorted representation space characterization through backpropagated gradients")
  Member "adversarially learned one class classifier for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "avid adversarial visual irregularity detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep hr fast heart rate estimation from face video under realistic conditions")
  Member "abnormal event detection in videos using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos")
  Member "unmasking the abnormal events in video":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")

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)