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 "from open set to closed set counting objects by spatial divide and conquer"):
  Abstract: Visual counting, a task that predicts the number of objects from an image/video, is an open-set problem by nature, i.e., the number of population can vary in $[0,+\infty)$ in theory. However, the collected images and labeled count values are limited in reality, which means only a small closed set is observed. Existing methods typically model this task in a regression manner, while they are likely to suffer from an unseen scene with counts out of the scope of the closed set. In fact, counting is decomposable. A dense region can always be divided until sub-region counts are within the previously observed closed set. Inspired by this idea, we propose a simple but effective approach, Spatial Divide-and- Conquer Network (S-DCNet). S-DCNet only learns from a closed set but can generalize well to open-set scenarios via S-DC. S-DCNet is also efficient. To avoid repeatedly computing sub-region convolutional features, S-DC is executed on the feature map instead of on the input image. S-DCNet achieves the state-of-the-art performance on three crowd counting datasets (ShanghaiTech, UCF_CC_50 and UCF-QNRF), a vehicle counting dataset (TRANCOS) and a plant counting dataset (MTC). Compared to the previous best methods, S-DCNet brings a 20.2% relative improvement on the ShanghaiTech Part B, 20.9% on the UCF-QNRF, 22.5% on the TRANCOS and 15.1% on the MTC. Code has been made available at: https://github. com/xhp-hust-2018-2011/S-DCNet.

Includes members:
  Member "divide and grow capturing huge diversity in crowd images with incrementally growing cnn":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "composition loss for counting density map estimation and localization in dense crowds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "deep attention based classification network for robust depth prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting")
  Member "where are the blobs counting by localization with point supervision":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "crowd scene analysis by output encoding")
  Member "deep ordinal regression network for monocular depth estimation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dsgn deep stereo geometry network for 3d object detection")
  Member "csrnet dilated convolutional neural networks for understanding the highly congested scenes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "iterative crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "switching convolutional neural network for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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)