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: "automated x ray image analysis for cargo security critical review and future promise"
  Abstract: We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-related threats are being smuggled across borders by organised crime and terrorist networks. We split the field into the classical pipeline of image preprocessing and image understanding. Preprocessing includes: image manipulation; quality improvement; Threat Image Projection (TIP); and material discrimination and segmentation. Image understanding includes: Automated Threat Detection (ATD); and Automated Contents Verification (ACV). We identify several gaps in the literature that need to be addressed and propose ideas for future research. Where the current literature is sparse we borrow from the single-view, multi-view, and CT X-ray baggage domains, which have some characteristics in common with X-ray cargo.

Connected through hyperedge HE-1 (induced by source paper "towards automatic threat detection a survey of advances of deep learning within x ray security imaging"):
  with members:
    - "evaluation of a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery"
    - "on the impact of object and sub component level segmentation strategies for supervised anomaly detection within x ray security imagery"
    - "toward automatic threat recognition for airport x ray baggage screening with deep convolutional object detection"
    - "analyzing and improving the image quality of stylegan"
    - "deep cmst framework for the autonomous recognition of heavily occluded and cluttered baggage items from multivendor security radiographs"
    - "sixray a large scale security inspection x ray benchmark for prohibited item discovery in overlapping images"
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "evaluating the transferability and adversarial discrimination of convolutional neural networks for threat object detection and classification within x ray security imagery"

Connected through hyperedge HE-2 (induced by source paper "toward automatic threat recognition for airport x ray baggage screening with deep convolutional object detection"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "learning transferable architectures for scalable image recognition"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "speed accuracy trade offs for modern convolutional object detectors"
    - "r fcn object detection via region based fully convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "evaluation of a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery"):
  with members:
    - "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "focal loss for dense object detection"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "mask r cnn"
    - "inception v4 inception resnet and the impact of residual connections on learning"

Connected through hyperedge HE-4 (induced by source paper "ganomaly semi supervised anomaly detection via adversarial training"):
  with members:
    - "efficient gan based anomaly detection"
    - "an overview of deep learning based methods for unsupervised and semi supervised anomaly detection in videos"
    - "generative adversarial networks an overview"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "improved training of wasserstein gans"
    - "precise recovery of latent vectors from generative adversarial networks"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "anomaly detection in video using predictive convolutional long short term memory networks"

Connected through hyperedge HE-5 (induced by source paper "automated detection of smuggled high risk security threats using deep learning"):
  with members:
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"
    - "matconvnet convolutional neural networks for matlab"

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)