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 "learning a layout transfer network for context aware object detection"):
  Abstract: We present a context aware object detection method based on a retrieve-and-transform scene layout model. Given an input image, our approach first retrieves a coarse scene layout from a codebook of typical layout templates. In order to handle large layout variations, we use a variant of the spatial transformer network to transform and refine the retrieved layout, resulting in a set of interpretable and semantically meaningful feature maps of object locations and scales. The above steps are implemented as a Layout Transfer Network which we integrate into Faster RCNN to allow for joint reasoning of object detection and scene layout estimation. Extensive experiments on three public datasets verified that our approach provides consistent performance improvements to the state-of-the-art object detection baselines on a variety of challenging tasks in the traffic surveillance and the autonomous driving domains.

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 "learning to look around objects for top view representations of outdoor scenes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "monolayout amodal scene layout from a single image")
  Member "a general pipeline for 3d detection of vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from points to parts 3d object detection from point cloud with part aware and part aggregation network")
  Member "frustum pointnets for 3d object detection from rgb d data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "review deep learning on 3d point clouds")
  Member "seeing what is not there learning context to determine where objects are missing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generating 3d people in scenes without people")
  Member "dsod learning deeply supervised object detectors from scratch":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")
  Member "foveanet perspective aware urban scene parsing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cars can t fly up in the sky improving urban scene segmentation via height driven attention networks")
  Member "deep manta a coarse to fine many task network for joint 2d and 3d vehicle analysis from monocular image":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")

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)