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 "training adversarial discriminators for cross channel abnormal event detection in crowds"):
  Abstract: Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios. However, the ambiguity and the lack of sufficient abnormal ground truth data makes end-to-end training of large deep networks hard in this domain. In this paper we propose to use Generative Adversarial Nets (GANs), which are trained to generate only the normal distribution of the data. During the adversarial GAN training, a discriminator (D) is used as a supervisor for the generator network (G) and vice versa. At testing time we use D to solve our discriminative task (abnormality detection), where D has been trained without the need of manually-annotated abnormal data. Moreover, in order to prevent G learn a trivial identity function, we use a cross-channel approach, forcing G to transform raw-pixel data in motion information and vice versa. The quantitative results on standard benchmarks show that our method outperforms previous state-of-the-art methods in both the frame-level and the pixel-level evaluation.

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 "precomputed real time texture synthesis with markovian generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "collaborative distillation for ultra resolution universal style transfer")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "emotion based crowd representation for abnormality detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "image to image translation with conditional adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "learning temporal regularity in video sequences":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection")
  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 "plug and play cnn for crowd motion analysis an application in abnormal event detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos")

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)