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 "lapnet automatic balanced loss and optimal assignment for real time dense object detection"):
  Abstract: Several modern deep single-stage object detectors are really effective for real time processing but still remain less efficient than more complex ones. The trade-off between model performances and computing speed is an important challenge, directly related to the learning process. In this paper, we propose a new way to efficiently learn a single shot detector providing a very good trade-off between these two factors. For this purpose, we introduce LapNet, an anchor based detector, trained end-to-end without any sampling strategy. Our approach focuses on two limitations of anchor based detector training: (1) the ambiguity of anchor to ground truth assignment and (2) the imbalance between classes and the imbalance between object sizes. More specifically, a new method to assign positive and negative anchors is proposed, based on a new overlapping function called "Per-Object Normalized Overlap" (PONO). This more flexible assignment can be self-corrected by the network itself to avoid the ambiguity between close objects. In the learning process, we also propose to automatically learn weights to balance classes and object sizes to efficiently manage sample imbalance. It allows to build a robust object detector avoiding multi-scale prediction, in a semantic segmentation spirit.

Includes members:
  Member "fcos fully convolutional one stage object detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "cornernet detecting objects as paired keypoints":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "focal loss for dense object detection":
    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 "dssd deconvolutional single shot detector":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural 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-7 (induced by source paper "rtm3d real time monocular 3d detection from object keypoints for autonomous driving")
  Member "aggregated residual transformations for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic future prediction for video scene understanding")

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)