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 "self trained deep ordinal regression for end to end video anomaly detection"):
  Abstract: Video anomaly detection is of critical practical importance to a variety of real applications because it allows human attention to be focused on events that are likely to be of interest, in spite of an otherwise overwhelming volume of video. We show that applying self-trained deep ordinal regression to video anomaly detection overcomes two key limitations of existing methods, namely, 1) being highly dependent on manually labeled normal training data; and 2) sub-optimal feature learning. By formulating a surrogate two-class ordinal regression task we devise an end-to-end trainable video anomaly detection approach that enables joint representation learning and anomaly scoring without manually labeled normal/abnormal data. Experiments on eight real-world video scenes show that our proposed method outperforms state-of-the-art methods that require no labeled training data by a substantial margin, and enables easy and accurate localization of the identified anomalies. Furthermore, we demonstrate that our method offers effective human-in-the-loop anomaly detection which can be critical in applications where anomalies are rare and the false-negative cost is high.

Includes members:
  Member "deep anomaly detection with deviation networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep weakly supervised anomaly detection")
  Member "anomaly detection in video sequence with appearance motion correspondence":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "history based anomaly detector an adversarial approach to anomaly detection")
  Member "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")
  Member "real world anomaly detection in surveillance videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "object centric auto encoders and dummy anomalies for abnormal event detection in video":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos")
  Member "detecting abnormal events in video using narrowed normality clusters":
    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 "adversarially learned one class classifier for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty detection")
  Member "latent space autoregression for novelty detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "compact surjective encoding autoencoder for unsupervised novelty 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)