human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "unmasking the abnormal events in video"
  Abstract: We propose a novel framework for abnormal event detection in video that requires no training sequences. Our framework is based on unmasking, a technique previously used for authorship verification in text documents, which we adapt to our task. We iteratively train a binary classifier to distinguish between two consecutive video sequences while removing at each step the most discriminant features. Higher training accuracy rates of the intermediately obtained classifiers represent abnormal events. To the best of our knowledge, this is the first work to apply unmasking for a computer vision task. We compare our method with several state-of-the-art supervised and unsupervised methods on four benchmark data sets. The empirical results indicate that our abnormal event detection framework can achieve state-of-the-art results, while running in real-time at 20 frames per second.

Connected through hyperedge HE-1 (induced by source paper "self trained deep ordinal regression for end to end video anomaly detection"):
  with members:
    - "deep anomaly detection with deviation networks"
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "real world anomaly detection in surveillance videos"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"

Connected through hyperedge HE-2 (induced by source paper "learning a distance function with a siamese network to localize anomalies in videos"):
  with members:
    - "street scene a new dataset and evaluation protocol for video anomaly detection"
    - "real world anomaly detection in surveillance videos"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "abnormal event detection in videos using generative adversarial nets"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "future frame prediction for anomaly detection a new baseline"

Connected through hyperedge HE-3 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "low shot learning from imaginary 3d model"
    - "self paced deep learning for weakly supervised object detection"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "experience replay for continual learning"
    - "hierarchy of gans for learning embodied self awareness model"
    - "a multi perspective approach to anomaly detection for self aware embodied agents"
    - "foil it find one mismatch between image and language caption"

Connected through hyperedge HE-4 (induced by source paper "anomaly detection in video sequence with appearance motion correspondence"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "adversarially learned one class classifier for novelty detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "future frame prediction for anomaly detection a new baseline"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"
    - "a discriminative framework for anomaly detection in large videos"

Connected through hyperedge HE-5 (induced by source paper "anomaly detection in road traffic using visual surveillance a survey"):
  with members:
    - "stan spatio temporal adversarial networks for abnormal event detection"
    - "real world anomaly detection in surveillance videos"
    - "review on computer vision techniques in emergency situations"
    - "robust deep and inductive anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "video captioning via hierarchical reinforcement learning"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-6 (induced by source paper "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"):
  with members:
    - "mptv matching pursuit based total variation minimization for image deconvolution"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "and autoregressive novelty detectors"
    - "anomaly detection using one class neural networks"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-7 (induced by source paper "fraud detection in networks state of the art"):
  with members:
    - "unsupervised dictionary learning for anomaly detection"
    - "spotting collective behaviour of online frauds in customer reviews"
    - "deep learning for anomaly detection a survey"
    - "midas microcluster based detector of anomalies in edge streams"
    - "a deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data"
    - "no place to hide catching fraudulent entities in tensors"
    - "anomaly detection using one class neural networks"
    - "one class adversarial nets for fraud detection"

Connected through hyperedge HE-8 (induced by source paper "street scene a new dataset and evaluation protocol for video anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "object centric auto encoders and dummy anomalies for abnormal event detection in video"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "abnormal event detection in videos using generative adversarial nets"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "future frame prediction for anomaly detection a new baseline"
    - "energy based models for video anomaly detection"
    - "generalized zero shot learning for action recognition with web scale video data"

Question: Please tell me which class the center node 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)