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: "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.

Connected through hyperedge HE-1 (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"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-2 (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-3 (induced by source paper "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "videos as space time region graphs"
    - "step by step erasion one by one collection a weakly supervised temporal action detector"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "single shot temporal action detection"
    - "temporal action detection with structured segment networks"
    - "temporal segment networks for action recognition in videos"
    - "multiple instance learning a survey of problem characteristics and applications"

Connected through hyperedge HE-4 (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"
    - "unmasking the abnormal events in video"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "robust and computationally efficient anomaly detection using powers of two networks"):
  with members:
    - "efficient gan based anomaly detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "a survey of model compression and acceleration for deep neural networks"
    - "context encoders feature learning by inpainting"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "conditional deep learning for energy efficient and enhanced pattern recognition"
    - "online sparse system identification and signal reconstruction using projections onto weighted ell_ 1 balls"

Connected through hyperedge HE-6 (induced by source paper "crowd density estimation using novel feature descriptor"):
  with members:
    - "towards a crowd analytic framework for crowd management in majid al haram"

Connected through hyperedge HE-7 (induced by source paper "and autoregressive novelty detectors"):
  with members:
    - "learning deep features for one class classification"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"
    - "few shot autoregressive density estimation towards learning to learn distributions"
    - "energy based models for video anomaly detection"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"

Connected through hyperedge HE-8 (induced by source paper "object centric auto encoders and dummy anomalies for abnormal event detection in video"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "deep clustering for unsupervised learning of visual features"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "abnormal event detection in videos using generative adversarial nets"
    - "unmasking the abnormal events in video"
    - "joint detection and recounting of abnormal events by learning deep generic knowledge"
    - "future frame prediction for anomaly detection a new baseline"
    - "a discriminative framework for anomaly detection in large videos"

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)