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: "deep cmst framework for the autonomous recognition of heavily occluded and cluttered baggage items from multivendor security radiographs"
  Abstract: Since the last two decades, luggage scanning has become one of the prime aviation security concerns all over the world. Manual screening of the baggage items is a cumbersome, subjective and inefficient process and many researchers have developed x-ray imagery based autonomous systems for this purpose. However, to the best of our knowledge, there is no framework till date which can recognize heavily occluded and cluttered baggage items from x-ray scans irrespective of the acquisition machinery or the scan modality. This paper presents a deep cascaded multiscale structure tensor-based framework which can automatically extract and recognize normal as well as suspicious items irrespective of their position and orientation from the multivendor x-ray scans. The proposed framework is unique in its kind as it intelligently extracts each object by iteratively picking contour based transitional information from different orientations and uses only a single feedforward convolutional neural network for the recognition. The proposed framework has been rigorously tested on two publicly available datasets containing cumulative of 1,067,381 x-ray scans where it significantly outperformed the existing state-of-the-art solutions by achieving the mean intersection-over-union ratings of up to 0.9689, area under the curve of up to 0.9950, accuracy of up to 0.9955 and the mean average precision score of up to 0.9453 for detecting normal as well as suspicious baggage items. Furthermore, the proposed framework has achieved 15.78% better time performance as compared to the popular object detectors.

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"
    - "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"

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)