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: "identifying and categorizing anomalies in retinal imaging data"
  Abstract: The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly supervised training enables the detection of findings that are known a priori. It does not scale well, and a priori definition limits the vocabulary of markers to known entities reducing the accuracy of diagnosis and prognosis. Here, we propose the identification of anomalies in large-scale medical imaging data using healthy examples as a reference. We detect and categorize candidates for anomaly findings untypical for the observed data. A deep convolutional autoencoder is trained on healthy retinal images. The learned model generates a new feature representation, and the distribution of healthy retinal patches is estimated by a one-class support vector machine. Results demonstrate that we can identify pathologic regions in images without using expert annotations. A subsequent clustering categorizes findings into clinically meaningful classes. In addition the learned features outperform standard embedding approaches in a classification task.

Connected through hyperedge HE-1 (induced by source paper "an alarm system for segmentation algorithm based on shape model"):
  with members:
    - "deep neural networks for no reference and full reference image quality assessment"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "uncertainty driven sanity check application to postoperative brain tumor cavity segmentation"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"
    - "real time prediction of segmentation quality"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "enhancing the reliability of out of distribution image detection in neural networks"

Connected through hyperedge HE-2 (induced by source paper "fast distance based anomaly detection in images using an inception like autoencoder"):
  with members:
    - "real world anomaly detection in surveillance videos"
    - "adversarially learned one class classifier for novelty detection"
    - "deep anomaly detection using geometric transformations"
    - "anomaly detection using deep learning based image completion"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "billion scale similarity search with gpus"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep structured energy based models for anomaly detection"

Connected through hyperedge HE-3 (induced by source paper "robust image segmentation quality assessment without ground truth"):
  with members:
    - "factorised spatial representation learning application in semi supervised myocardial segmentation"
    - "unsupervised detection of lesions in brain mri using constrained adversarial auto encoders"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "real time prediction of segmentation quality"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "reverse classification accuracy predicting segmentation performance in the absence of ground truth"
    - "adversarial examples in the physical world"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-4 (induced by source paper "time series anomaly detection with variational autoencoders"):
  with members:
    - "an improvement of paa on trend based approximation for time series"
    - "adversarially learned anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "a survey on deep learning in medical image analysis"
    - "real time anomaly detection for streaming analytics"

Connected through hyperedge HE-5 (induced by source paper "unsupervised identification of disease marker candidates in retinal oct imaging data"):
  with members:
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "unsupervised visual representation learning by context prediction"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "and autoregressive novelty detectors"):
  with members:
    - "learning deep features for one class classification"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"
    - "few shot autoregressive density estimation towards learning to learn distributions"
    - "energy based models for video anomaly detection"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"

Connected through hyperedge HE-7 (induced by source paper "doping generative data augmentation for unsupervised anomaly detection with gan"):
  with members:
    - "efficient gan based anomaly detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "data augmentation in emotion classification using generative adversarial networks"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "limiting the reconstruction capability of generative neural network using negative learning"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "rendergan generating realistic labeled data"
    - "adversarial feature learning"

Connected through hyperedge HE-8 (induced by source paper "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"):
  with members:
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using spatiotemporal autoencoder"
    - "adversarial feature learning"
    - "learning temporal regularity in video sequences"
    - "context encoders feature learning by inpainting"
    - "autoencoding beyond pixels using a learned similarity metric"

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.LG(Machine Learning)