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: "real world anomaly detection in surveillance videos"
  Abstract: Surveillance videos are able to capture a variety of realistic anomalies. In this paper, we propose to learn anomalies by exploiting both normal and anomalous videos. To avoid annotating the anomalous segments or clips in training videos, which is very time consuming, we propose to learn anomaly through the deep multiple instance ranking framework by leveraging weakly labeled training videos, i.e. the training labels (anomalous or normal) are at video-level instead of clip-level. In our approach, we consider normal and anomalous videos as bags and video segments as instances in multiple instance learning (MIL), and automatically learn a deep anomaly ranking model that predicts high anomaly scores for anomalous video segments. Furthermore, we introduce sparsity and temporal smoothness constraints in the ranking loss function to better localize anomaly during training. We also introduce a new large-scale first of its kind dataset of 128 hours of videos. It consists of 1900 long and untrimmed real-world surveillance videos, with 13 realistic anomalies such as fighting, road accident, burglary, robbery, etc. as well as normal activities. This dataset can be used for two tasks. First, general anomaly detection considering all anomalies in one group and all normal activities in another group. Second, for recognizing each of 13 anomalous activities. Our experimental results show that our MIL method for anomaly detection achieves significant improvement on anomaly detection performance as compared to the state-of-the-art approaches. We provide the results of several recent deep learning baselines on anomalous activity recognition. The low recognition performance of these baselines reveals that our dataset is very challenging and opens more opportunities for future work.

Connected through hyperedge HE-1 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "sports camera calibration via synthetic data"
    - "pose flow efficient online pose tracking"
    - "memory attention networks for skeleton based action recognition"

Connected through hyperedge HE-2 (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"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "adversarially learned one class classifier for novelty detection"
    - "latent space autoregression for novelty detection"

Connected through hyperedge HE-3 (induced by source paper "collaborative motion prediction via neural motion message passing"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "deepgcns can gcns go as deep as cnns"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "linknet relational embedding for scene graph"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-4 (induced by source paper "patternless adversarial attacks on video recognition networks"):
  with members:
    - "targeted nonlinear adversarial perturbations in images and videos"
    - "sparse adversarial perturbations for videos"
    - "slowfast networks for video recognition"
    - "adversarial perturbations against real time video classification systems"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "non local neural networks"
    - "a survey on deep learning in medical image analysis"
    - "flownet 2 0 evolution of optical flow estimation with deep networks"

Connected through hyperedge HE-5 (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"
    - "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"
    - "unmasking the abnormal events in video"

Connected through hyperedge HE-6 (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"
    - "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"
    - "future frame prediction for anomaly detection a new baseline"

Connected through hyperedge HE-7 (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"
    - "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 temporal regularity in video sequences"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "training free monocular 3d event detection system for traffic surveillance"):
  with members:
    - "traffic danger recognition with surveillance cameras without training data"
    - "simple online and realtime tracking with a deep association metric"
    - "traffic surveillance camera calibration by 3d model bounding box alignment for accurate vehicle speed measurement"
    - "mask r cnn"
    - "feature pyramid networks for object detection"

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)