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: "avid adversarial visual irregularity detection"
  Abstract: Real-time detection of irregularities in visual data is very invaluable and useful in many prospective applications including surveillance, patient monitoring systems, etc. With the surge of deep learning methods in the recent years, researchers have tried a wide spectrum of methods for different applications. However, for the case of irregularity or anomaly detection in videos, training an end-to-end model is still an open challenge, since often irregularity is not well-defined and there are not enough irregular samples to use during training. In this paper, inspired by the success of generative adversarial networks (GANs) for training deep models in unsupervised or self-supervised settings, we propose an end-to-end deep network for detection and fine localization of irregularities in videos (and images). Our proposed architecture is composed of two networks, which are trained in competing with each other while collaborating to find the irregularity. One network works as a pixel-level irregularity Inpainter, and the other works as a patch-level Detector. After an adversarial self-supervised training, in which I tries to fool D into accepting its inpainted output as regular (normal), the two networks collaborate to detect and fine-segment the irregularity in any given testing video. Our results on three different datasets show that our method can outperform the state-of-the-art and fine-segment the irregularity.

Connected through hyperedge HE-1 (induced by source paper "deep hr fast heart rate estimation from face video under realistic conditions"):
  with members:
    - "receptive field block net for accurate and fast object detection"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-2 (induced by source paper "self supervised representation learning via neighborhood relational encoding"):
  with members:
    - "variational autoencoder with truncated mixture of gaussians for functional connectivity analysis"
    - "deep clustering for unsupervised learning of visual features"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarially learned one class classifier for novelty detection"
    - "unsupervised representation learning by predicting image rotations"
    - "self supervised feature learning by learning to spot artifacts"
    - "unsupervised person re identification by deep learning tracklet association"
    - "magnet a two pronged defense against adversarial examples"

Connected through hyperedge HE-3 (induced by source paper "a promotion method for generation error based video anomaly detection"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "video anomaly detection and localization via gaussian mixture fully convolutional variational autoencoder"
    - "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"
    - "abnormal event detection in videos using spatiotemporal autoencoder"

Connected through hyperedge HE-4 (induced by source paper "adversarially learned abnormal trajectory classifier"):
  with members:
    - "road user abnormal trajectory detection using a deep autoencoder"
    - "adversarially learned one class classifier for novelty detection"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "adversarial feature learning"

Connected through hyperedge HE-5 (induced by source paper "autoids auto encoder based method for intrusion detection system"):
  with members:
    - "end to end adversarial learning for intrusion detection in computer networks"
    - "adversarially learned one class classifier for novelty detection"
    - "shallow and deep networks intrusion detection system a taxonomy and survey"

Connected through hyperedge HE-6 (induced by source paper "end to end adversarial learning for intrusion detection in computer networks"):
  with members:
    - "adversarially learned one class classifier for novelty 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)