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: "evaluation of a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery"
  Abstract: X-ray baggage security screening is widely used to maintain aviation and transport security. Of particular interest is the focus on automated security X-ray analysis for particular classes of object such as electronics, electrical items, and liquids. However, manual inspection of such items is challenging when dealing with potentially anomalous items. Here we present a dual convolutional neural network (CNN) architecture for automatic anomaly detection within complex security X-ray imagery. We leverage recent advances in region-based (R-CNN), mask-based CNN (Mask R-CNN) and detection architectures such as RetinaNet to provide object localisation variants for specific object classes of interest. Subsequently, leveraging a range of established CNN object and fine-grained category classification approaches we formulate within object anomaly detection as a two-class problem (anomalous or benign). While the best performing object localisation method is able to perform with 97.9% mean average precision (mAP) over a six-class X-ray object detection problem, subsequent two-class anomaly/benign classification is able to achieve 66% performance for within object anomaly detection. Overall, this performance illustrates both the challenge and promise of object-wise anomaly detection within the context of cluttered X-ray security imagery.

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:
    - "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"
    - "yolov3 an incremental improvement"

Connected through hyperedge HE-2 (induced by source paper "cascaded structure tensor framework for robust identification of heavily occluded baggage items from multi vendor x ray scans"):
  with members:
    - "deep learning in video multi object tracking a survey"
    - "a survey of deep learning based object detection"
    - "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"
    - "occlusion aware r cnn detecting pedestrians in a crowd"
    - "chest x ray inpainting with deep generative models"
    - "deep affinity network for multiple object tracking"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"

Connected through hyperedge HE-3 (induced by source paper "evaluating the transferability and adversarial discrimination of convolutional neural networks for threat object detection and classification within x ray security imagery"):
  with members:
    - "sixray a large scale security inspection x ray benchmark for prohibited item discovery in overlapping images"
    - "focal loss for dense object detection"
    - "mask r cnn"
    - "yolo9000 better faster stronger"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"

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)