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 "fully convolutional neural network for fast anomaly detection in crowded scenes"):
  Abstract: We present an efficient method for detecting and localizing anomalies in videos showing crowded scenes. Research on {\it fully convolutional neural networks} (FCNs) has shown the potentials of this technology for object detection and localization, especially in images. We investigate how to involve temporal data, and how to transform a supervised FCN into an unsupervised one such that the resulting FCN ensures anomaly detection. Altogether, we propose an FCN-based architecture for anomaly detection and localization in crowded scenes videos. For reducing computations and, consequently, improving performance both with respect to speed and accuracy, we investigate the use of cascaded out-layer detection. Our architecture includes two main components, one for feature representation, and one for cascaded out-layer detection. Experimental results on Subway and UCSD benchmarks confirm that the detection and localization accuracy of our method is comparable to state-of-the-art methods, but at a significantly increased speed of 370 fps.

Includes members:
  Member "learning deep representations of appearance and motion for anomalous event detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection")
  Member "deepproposal hunting objects by cascading deep convolutional layers":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cascade retinanet maintaining consistency for single stage object detection")
  Member "faster r cnn towards real time object detection with region proposal networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "two stream convolutional networks for action recognition in videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "overfeat integrated recognition localization and detection using convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "fast image scanning with deep max pooling convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "arma nets expanding receptive field for dense prediction")

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)