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: "training adversarial discriminators for cross channel abnormal event detection in crowds"
  Abstract: Abnormal crowd behaviour detection attracts a large interest due to its importance in video surveillance scenarios. However, the ambiguity and the lack of sufficient abnormal ground truth data makes end-to-end training of large deep networks hard in this domain. In this paper we propose to use Generative Adversarial Nets (GANs), which are trained to generate only the normal distribution of the data. During the adversarial GAN training, a discriminator (D) is used as a supervisor for the generator network (G) and vice versa. At testing time we use D to solve our discriminative task (abnormality detection), where D has been trained without the need of manually-annotated abnormal data. Moreover, in order to prevent G learn a trivial identity function, we use a cross-channel approach, forcing G to transform raw-pixel data in motion information and vice versa. The quantitative results on standard benchmarks show that our method outperforms previous state-of-the-art methods in both the frame-level and the pixel-level evaluation.

Connected through hyperedge HE-1 (induced by source paper "anomaly detection in video data based on probabilistic latent space models"):
  with members:
    - "continual lifelong learning with neural networks a review"
    - "the limits and potentials of deep learning for robotics"
    - "hierarchy of gans for learning embodied self awareness model"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "action recognition from static datasets to moving robots"
    - "comparing deep neural networks against humans object recognition when the signal gets weaker"
    - "a study and comparison of human and deep learning recognition performance under visual distortions"

Connected through hyperedge HE-2 (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-3 (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"
    - "avid adversarial visual irregularity 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"

Connected through hyperedge HE-4 (induced by source paper "amil adversarial multi instance learning for human pose estimation"):
  with members:
    - "semi and weakly supervised human pose estimation"
    - "attention based deep multiple instance learning"
    - "densepose dense human pose estimation in the wild"
    - "dual discriminator generative adversarial nets"
    - "began boundary equilibrium generative adversarial networks"
    - "benchmarking and error diagnosis in multi instance pose estimation"
    - "dynamic routing between capsules"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"

Connected through hyperedge HE-5 (induced by source paper "combinational class activation maps for weakly supervised object localization"):
  with members:
    - "residual non local attention networks for image restoration"
    - "semantic matching by weakly supervised 2d point set registration"
    - "ficklenet weakly and semi supervised semantic image segmentation using stochastic inference"
    - "long term feature banks for detailed video understanding"
    - "non local netvlad encoding for video classification"
    - "self produced guidance for weakly supervised object localization"
    - "adversarial complementary learning for weakly supervised object localization"
    - "non locally enhanced encoder decoder network for single image de raining"

Connected through hyperedge HE-6 (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"
    - "avid adversarial visual irregularity 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"

Connected through hyperedge HE-7 (induced by source paper "generative adversarial networks for distributed intrusion detection in the internet of things"):
  with members:
    - "a vision of 6g wireless systems applications trends technologies and open research problems"
    - "efficient gan based anomaly detection"
    - "md gan multi discriminator generative adversarial networks for distributed datasets"
    - "mdgan boosting anomaly detection using multi discriminator generative adversarial networks"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "deep learning for signal authentication and security in massive internet of things systems"
    - "a tutorial on uavs for wireless networks applications challenges and open problems"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"

Connected through hyperedge HE-8 (induced by source paper "fence gan towards better anomaly detection"):
  with members:
    - "robust anomaly detection in images using adversarial autoencoders"
    - "efficient gan based anomaly detection"
    - "ganomaly semi supervised anomaly detection via adversarial training"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "doping generative data augmentation for unsupervised anomaly detection with gan"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "good semi supervised learning that requires a bad gan"
    - "deep structured energy based models for anomaly 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)