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 "mixture dense regression for object detection and human pose estimation"):
  Abstract: Mixture models are well-established machine learning approaches that, in computer vision, have mostly been applied to inverse or ill-defined problems. However, they are general-purpose divide-and-conquer techniques, splitting the input space into relatively homogeneous subsets, in a data-driven manner. Therefore, not only ill-defined but also well-defined complex problems should benefit from them. To this end, we devise a multi-modal solution for spatial regression using mixture density networks for dense object detection and human pose estimation. For both tasks, we show that a mixture model converges faster, yields higher accuracy, and divides the input space into interpretable modes. For object detection, mixture components learn to focus on object scale with the distribution of components closely following the distribution of ground truth object scale. For human pose estimation, a mixture model divides the data based on viewpoint and uncertainty -- namely, front and back views, with back view imposing higher uncertainty. We conduct our experiments on the MS COCO dataset and do not face any mode collapse. However, to avoid numerical instabilities, we had to modify the activation function for the mixture variance terms slightly.

Includes members:
  Member "rethinking on multi stage networks for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning delicate local representations for multi person pose estimation")
  Member "lvis a dataset for large vocabulary instance segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "equalization loss for long tailed object recognition")
  Member "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "the garden of forking paths towards multi future trajectory prediction")
  Member "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "monopair monocular 3d object detection using pairwise spatial relationships")
  Member "feature selective anchor free module for single shot object detection":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "siamese box adaptive network for visual tracking")
  Member "unsupervised discovery of parts structure and dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep variational luenberger type observer for stochastic video prediction")
  Member "pifpaf composite fields for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "higherhrnet scale aware representation learning for bottom up human pose estimation")
  Member "scale aware trident networks for object detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "revisiting the sibling head in object detector")

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)