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 using deep learning based image completion"
  Abstract: Automated surface inspection is an important task in many manufacturing industries and often requires machine learning driven solutions. Supervised approaches, however, can be challenging, since it is often difficult to obtain large amounts of labeled training data. In this work, we instead perform one-class unsupervised learning on fault-free samples by training a deep convolutional neural network to complete images whose center regions are cut out. Since the network is trained exclusively on fault-free data, it completes the image patches with a fault-free version of the missing image region. The pixel-wise reconstruction error within the cut out region is an anomaly image which can be used for anomaly detection. Results on surface images of decorated plastic parts demonstrate that this approach is suitable for detection of visible anomalies and moreover surpasses all other tested methods.

Connected through hyperedge HE-1 (induced by source paper "synthesize then compare detecting failures and anomalies for semantic segmentation"):
  with members:
    - "a benchmark for anomaly segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "mask scoring r cnn"
    - "semantic image synthesis with spatially adaptive normalization"
    - "learning to predict layout to image conditional convolutions for semantic image synthesis"
    - "an alarm system for segmentation algorithm based on shape model"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-2 (induced by source paper "safe robot navigation via multi modal anomaly detection"):
  with members:
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "gonet a semi supervised deep learning approach for traversability estimation"
    - "why relu networks yield high confidence predictions far away from the training data and how to mitigate the problem"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "density estimation using real nvp"
    - "find your own way weakly supervised segmentation of path proposals for urban autonomy"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "a benchmark for anomaly segmentation"):
  with members:
    - "tencent ml images a large scale multi label image database for visual representation learning"
    - "learning rich features for image manipulation detection"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deep autoencoding models for unsupervised anomaly segmentation in brain mr images"
    - "deep anomaly detection with outlier exposure"
    - "the open images dataset v4 unified image classification object detection and visual relationship detection at scale"
    - "enhancing the reliability of out of distribution image detection in neural networks"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-4 (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"
    - "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"
    - "identifying and categorizing anomalies in retinal imaging data"

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)