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: "object centric auto encoders and dummy anomalies for abnormal event detection in video"
  Abstract: Abnormal event detection in video is a challenging vision problem. Most existing approaches formulate abnormal event detection as an outlier detection task, due to the scarcity of anomalous data during training. Because of the lack of prior information regarding abnormal events, these methods are not fully-equipped to differentiate between normal and abnormal events. In this work, we formalize abnormal event detection as a one-versus-rest binary classification problem. Our contribution is two-fold. First, we introduce an unsupervised feature learning framework based on object-centric convolutional auto-encoders to encode both motion and appearance information. Second, we propose a supervised classification approach based on clustering the training samples into normality clusters. A one-versus-rest abnormal event classifier is then employed to separate each normality cluster from the rest. For the purpose of training the classifier, the other clusters act as dummy anomalies. During inference, an object is labeled as abnormal if the highest classification score assigned by the one-versus-rest classifiers is negative. Comprehensive experiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and UMN. Our approach provides superior results on all four data sets. On the large-scale ShanghaiTech data set, our method provides an absolute gain of 8.4% in terms of frame-level AUC compared to the state-of-the-art method [Sultani et al., CVPR 2018].

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"
    - "detecting abnormal events in video using narrowed normality clusters"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"
    - "unmasking the abnormal events in video"

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"
    - "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"
    - "future frame prediction for anomaly detection a new baseline"

Connected through hyperedge HE-3 (induced by source paper "convolutional neural networks with intermediate loss for 3d super resolution of ct and mri scans"):
  with members:
    - "super resolution of brain mri images using overcomplete dictionaries and nonlocal similarity"
    - "a tensor factorization method for 3 d super resolution with application to dental ct"
    - "image super resolution using very deep residual channel attention networks"
    - "channel splitting network for single mr image super resolution"
    - "lstm fully convolutional networks for time series classification"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "enhanced deep residual networks for single image super resolution"
    - "simultaneous super resolution and cross modality synthesis of 3d medical images using weakly supervised joint convolutional sparse coding"

Connected through hyperedge HE-4 (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"
    - "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"
    - "nisp pruning networks using neuron importance score propagation"

Connected through hyperedge HE-5 (induced by source paper "history based anomaly detector an adversarial approach to anomaly detection"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "a survey on gans for anomaly detection"
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "deep structured cross modal anomaly detection"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "input complexity and out of distribution detection with likelihood based generative models"
    - "deep learning for anomaly detection a survey"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"

Connected through hyperedge HE-6 (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"
    - "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"
    - "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)