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 "orientation aware object detection with application to firearms"):
  Abstract: Automatic detection of firearms is important for enhancing security and safety of people, however, it is a challenging task owing to the wide variations in shape, size and appearance of firearms. To handle these challenges we propose an Orientation Aware Object Detector (OAOD) which has achieved improved firearm detection and localization performance. The proposed detector has two phases. In the Phase-1 it predicts orientation of the object which is used to rotate the object proposal. Maximum area rectangles are cropped from the rotated object proposals which are again classified and localized in the Phase-2 of the algorithm. The oriented object proposals are mapped back to the original coordinates resulting in oriented bounding boxes which localize the weapons much better than the axis aligned bounding boxes. Being orientation aware, our non-maximum suppression is able to avoid multiple detection of the same object and it can better resolve objects which lie in close proximity to each other. This two phase system leverages OAOD to predict object oriented bounding boxes while being trained only on the axis aligned boxes in the ground-truth. In order to train object detectors for firearm detection, a dataset consisting of around eleven thousand firearm images is collected from the internet and manually annotated. The proposed ITU Firearm (ITUF) dataset contains wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state of the art object detectors. Our experiments demonstrate the excellent performance of the proposed detector for the task of firearm detection.

Includes members:
  Member "sinet a scale insensitive convolutional neural network for fast vehicle detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "pixel semantic revise of position learning a one stage object detector with a shared encoder decoder")
  Member "towards multi class object detection in unconstrained remote sensing imagery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "arbitrary oriented object detection with circular smooth label")
  Member "cascade r cnn delving into high quality object detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "proposal learning for semi supervised object detection")
  Member "dssd deconvolutional single shot detector":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "perceptual generative adversarial networks for small object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "extended feature pyramid network for small object detection")
  Member "mask r cnn":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "speed accuracy trade offs for modern convolutional object detectors":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "progressive gradient pruning for classification detection and domainadaptation")
  Member "feature pyramid networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")

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)