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: "anomaly detection in video sequence with appearance motion correspondence"
  Abstract: Anomaly detection in surveillance videos is currently a challenge because of the diversity of possible events. We propose a deep convolutional neural network (CNN) that addresses this problem by learning a correspondence between common object appearances (e.g. pedestrian, background, tree, etc.) and their associated motions. Our model is designed as a combination of a reconstruction network and an image translation model that share the same encoder. The former sub-network determines the most significant structures that appear in video frames and the latter one attempts to associate motion templates to such structures. The training stage is performed using only videos of normal events and the model is then capable to estimate frame-level scores for an unknown input. The experiments on 6 benchmark datasets demonstrate the competitive performance of the proposed approach with respect to state-of-the-art methods.

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"
    - "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"
    - "unmasking the abnormal events in video"

Connected through hyperedge HE-2 (induced by source paper "history based anomaly detector an adversarial approach to anomaly detection"):
  with members:
    - "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"
    - "learning rich features for image manipulation detection"

Connected through hyperedge HE-3 (induced by source paper "spatio temporal adversarial learning for detecting unseen falls"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "robust anomaly detection in images using adversarial autoencoders"
    - "stan spatio temporal adversarial networks for abnormal event detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "satellite image forgery detection and localization using gan and one class classifier"
    - "adversarially learned one class classifier for novelty detection"
    - "deepfall non invasive fall detection with deep spatio temporal convolutional autoencoders"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-4 (induced by source paper "the liar s walk detecting deception with gait and gesture"):
  with members:
    - "learning regularity in skeleton trajectories for anomaly detection in videos"
    - "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"

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)