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: "learning temporal regularity in video sequences"
  Abstract: Perceiving meaningful activities in a long video sequence is a challenging problem due to ambiguous definition of 'meaningfulness' as well as clutters in the scene. We approach this problem by learning a generative model for regular motion patterns, termed as regularity, using multiple sources with very limited supervision. Specifically, we propose two methods that are built upon the autoencoders for their ability to work with little to no supervision. We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder on them. Second, we build a fully convolutional feed-forward autoencoder to learn both the local features and the classifiers as an end-to-end learning framework. Our model can capture the regularities from multiple datasets. We evaluate our methods in both qualitative and quantitative ways - showing the learned regularity of videos in various aspects and demonstrating competitive performance on anomaly detection datasets as an application.

Connected through hyperedge HE-1 (induced by source paper "arae adversarially robust training of autoencoders improves novelty detection"):
  with members:
    - "adversarial training and robustness for multiple perturbations"
    - "deep learning for anomaly detection a survey"
    - "ocgan one class novelty detection using gans with constrained latent representations"
    - "memorizing normality to detect anomaly memory augmented deep autoencoder for unsupervised anomaly detection"
    - "rare event detection using disentangled representation learning"
    - "real world anomaly detection in surveillance videos"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"

Connected through hyperedge HE-2 (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-3 (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-4 (induced by source paper "3d resnet with ranking loss function for abnormal activity detection in videos"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "real world anomaly detection in surveillance videos"
    - "are pre trained cnns good feature extractors for anomaly detection in surveillance videos"
    - "learning spatio temporal features with 3d residual networks for action recognition"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "the kinetics human action video dataset"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-5 (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-6 (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-7 (induced by source paper "towards corner case detection for autonomous driving"):
  with members:
    - "pixel deconvolutional networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "lidar and camera detection fusion in a real time industrial multi sensor collision avoidance system"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "future frame prediction for anomaly detection a new baseline"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-8 (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"
    - "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"
    - "mask r cnn"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

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)