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 regularity in skeleton trajectories for anomaly detection in videos"
  Abstract: Appearance features have been widely used in video anomaly detection even though they contain complex entangled factors. We propose a new method to model the normal patterns of human movements in surveillance video for anomaly detection using dynamic skeleton features. We decompose the skeletal movements into two sub-components: global body movement and local body posture. We model the dynamics and interaction of the coupled features in our novel Message-Passing Encoder-Decoder Recurrent Network. We observed that the decoupled features collaboratively interact in our spatio-temporal model to accurately identify human-related irregular events from surveillance video sequences. Compared to traditional appearance-based models, our method achieves superior outlier detection performance. Our model also offers "open-box" examination and decision explanation made possible by the semantically understandable features and a network architecture supporting interpretability.

Connected through hyperedge HE-1 (induced by source paper "3d resnet with ranking loss function for abnormal activity detection in videos"):
  with members:
    - "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 temporal regularity in video sequences"
    - "learning spatiotemporal features with 3d convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "learning to reason with relational video representation for question answering"):
  with members:
    - "progressive attention memory network for movie story question answering"
    - "neural symbolic vqa disentangling reasoning from vision and language understanding"
    - "motion appearance co memory networks for video question answering"
    - "multimodal dual attention memory for video story question answering"
    - "compositional attention networks for machine reasoning"
    - "variational memory encoder decoder"
    - "tvqa localized compositional video question answering"
    - "movie question answering remembering the textual cues for layered visual contents"

Connected through hyperedge HE-3 (induced by source paper "graph embedded pose clustering for anomaly detection"):
  with members:
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "pose flow efficient online pose tracking"
    - "deep clustering for unsupervised learning of visual features"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "latent space autoregression for novelty detection"
    - "abnormal event detection in videos using generative adversarial nets"
    - "attention is all you need"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-4 (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"
    - "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"
    - "learning rich features for image manipulation detection"

Connected through hyperedge HE-5 (induced by source paper "the liar s walk detecting deception with gait and gesture"):
  with members:
    - "anomaly detection in video sequence with appearance motion correspondence"
    - "context aware emotion recognition networks"
    - "step spatial temporal graph convolutional networks for emotion perception from gaits"
    - "gait recognition via disentangled representation learning"
    - "identifying emotions from walking using affective and deep features"
    - "pose guided human video generation"
    - "real world anomaly detection in surveillance videos"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"

Connected through hyperedge HE-6 (induced by source paper "multi timescale trajectory prediction for abnormal human activity detection"):
  with members:
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "latent space autoregression for novelty detection"
    - "real time multiple people tracking with deeply learned candidate selection and person re identification"
    - "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"
    - "ssh single stage headless face detector"
    - "future frame prediction for anomaly detection a new baseline"

Connected through hyperedge HE-7 (induced by source paper "image based table recognition data model and evaluation"):
  with members:
    - "tablenet deep learning model for end to end table detection and tabular data extraction from scanned document images"
    - "publaynet largest dataset ever for document layout analysis"
    - "rethinking table recognition using graph neural networks"
    - "extracting scientific figures with distantly supervised neural networks"
    - "corpus conversion service a machine learning platform to ingest documents at scale"
    - "mask r cnn"
    - "image to markup generation with coarse to fine attention"
    - "you only look once unified real time 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)