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 "history based anomaly detector an adversarial approach to anomaly detection"):
  Abstract: Anomaly detection is a difficult problem in many areas and has recently been subject to a lot of attention. Classifying unseen data as anomalous is a challenging matter. Latest proposed methods rely on Generative Adversarial Networks (GANs) to estimate the normal data distribution, and produce an anomaly score prediction for any given data. In this article, we propose a simple yet new adversarial method to tackle this problem, denoted as History-based anomaly detector (HistoryAD). It consists of a self-supervised model, trained to recognize 'normal' samples by comparing them to samples based on the training history of a previously trained GAN. Quantitative and qualitative results are presented evaluating its performance. We also present a comparison to several state-of-the-art methods for anomaly detection showing that our proposal achieves top-tier results on several datasets.

Includes members:
  Member "anomaly detection in video sequence with appearance motion correspondence":
    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 "a survey on gans for anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fixing bias in reconstruction based anomaly detection with lipschitz discriminators")
  Member "learning regularity in skeleton trajectories for anomaly detection in videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d resnet with ranking loss function for abnormal activity detection in videos")
  Member "deep structured cross modal anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pyodds an end to end outlier detection system with automated machine learning")
  Member "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "input complexity and out of distribution detection with likelihood based generative models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "likelihood regret an out of distribution detection score for variational auto encoder")
  Member "deep learning for anomaly detection a survey":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "anomalous instance detection in deep learning a survey")
  Member "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "memorizing comprehensively to learn adaptively unsupervised cross domain person re id with multi level memory")

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)