human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "towards automatic threat detection a survey of advances of deep learning within x ray security imaging"):
  Abstract: X-ray security screening is widely used to maintain aviation/transport security, and its significance poses a particular interest in automated screening systems. This paper aims to review computerised X-ray security imaging algorithms by taxonomising the field into conventional machine learning and contemporary deep learning applications. The first part briefly discusses the classical machine learning approaches utilised within X-ray security imaging, while the latter part thoroughly investigates the use of modern deep learning algorithms. The proposed taxonomy sub-categorises the use of deep learning approaches into supervised, semi-supervised and unsupervised learning, with a particular focus on object classification, detection, segmentation and anomaly detection tasks. The paper further explores well-established X-ray datasets and provides a performance benchmark. Based on the current and future trends in deep learning, the paper finally presents a discussion and future directions for X-ray security imagery.

Includes members:
  Member "evaluation of a dual convolutional neural network architecture for object wise anomaly detection in cluttered x ray security imagery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cascaded structure tensor framework for robust identification of heavily occluded baggage items from multi vendor x ray scans")
  Member "on the impact of object and sub component level segmentation strategies for supervised anomaly detection within x ray security imagery":
    also connected through other hyperedges:
      (none)
  Member "toward automatic threat recognition for airport x ray baggage screening with deep convolutional object detection":
    also connected through other hyperedges:
      (none)
  Member "analyzing and improving the image quality of stylegan":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deepfakes and beyond a survey of face manipulation and fake detection")
  Member "deep cmst framework for the autonomous recognition of heavily occluded and cluttered baggage items from multivendor security radiographs":
    also connected through other hyperedges:
      (none)
  Member "sixray a large scale security inspection x ray benchmark for prohibited item discovery in overlapping images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the good the bad and the ugly evaluating convolutional neural networks for prohibited item detection using real and synthetically composited x ray imagery")
  Member "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cascaded structure tensor framework for robust identification of heavily occluded baggage items from multi vendor x ray scans")
  Member "evaluating the transferability and adversarial discrimination of convolutional neural networks for threat object detection and classification within x ray security imagery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cascaded structure tensor framework for robust identification of heavily occluded baggage items from multi vendor x ray scans")

Question: Please tell me which class the source paper 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)