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 "plug and play cnn for crowd motion analysis an application in abnormal event detection"):
  Abstract: Most of the crowd abnormal event detection methods rely on complex hand-crafted features to represent the crowd motion and appearance. Convolutional Neural Networks (CNN) have shown to be a powerful tool with excellent representational capacities, which can leverage the need for hand-crafted features. In this paper, we show that keeping track of the changes in the CNN feature across time can facilitate capturing the local abnormality. We specifically propose a novel measure-based method which allows measuring the local abnormality in a video by combining semantic information (inherited from existing CNN models) with low-level Optical-Flow. One of the advantage of this method is that it can be used without the fine-tuning costs. The proposed method is validated on challenging abnormality detection datasets and the results show the superiority of our method compared to the state-of-the-art methods.

Includes members:
  Member "abnormal event detection in videos using generative adversarial nets":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos")
  Member "abnormal event detection in videos using spatiotemporal autoencoder":
    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 "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "training adversarial discriminators for cross channel abnormal event detection in crowds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "anomaly detection in video data based on probabilistic latent space models")
  Member "a discriminative framework for anomaly detection in large videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "emotion based crowd representation for abnormality detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "fully convolutional neural network for fast anomaly detection in crowded scenes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "cnn aware binary map for general semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")

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)