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 "cascaded structure tensor framework for robust identification of heavily occluded baggage items from multi vendor x ray scans"):
  Abstract: In the last two decades, luggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is a cumbersome, subjective and inefficient process. Hence, many researchers have developed Xray imagery-based autonomous systems to address these shortcomings. However, to the best of our knowledge, there is no framework, up to now, that can recognize heavily occluded and cluttered baggage items from multi-vendor X-ray scans. This paper presents a cascaded structure tensor framework which can automatically extract and recognize suspicious items irrespective of their position and orientation in the multi-vendor X-ray scans. The proposed framework is unique, 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 publicly available GDXray and SIXray datasets containing a total of 1,067,381 X-ray scans where it significantly outperformed the state-of-the-art solutions by achieving the mean average precision score of 0.9343 and 0.9595 for extracting and recognizing suspicious items from GDXray and SIXray scans, respectively. Furthermore, the proposed framework has achieved 15.78% better time

Includes members:
  Member "deep learning in video multi object tracking a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi object tracking via end to end tracklet searching and ranking")
  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-2 (induced by source paper "towards automatic threat detection a survey of advances of deep learning within x ray security imaging")
  Member "a survey of deep learning based object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "skip ganomaly skip connected and adversarially trained encoder decoder anomaly detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards automatic threat detection a survey of advances of deep learning within x ray security imaging")
  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 "towards automatic threat detection a survey of advances of deep learning within x ray security imaging")
  Member "occlusion aware r cnn detecting pedestrians in a crowd":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "chest x ray inpainting with deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep cmst framework for the autonomous recognition of heavily occluded and cluttered baggage items from multivendor security radiographs")
  Member "deep affinity network for multiple object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "panda a gigapixel level human centric video dataset")

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)