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 "segmentation guided attention network for crowd counting via curriculum learning"):
  Abstract: Crowd counting using deep convolutional neural networks (CNN) has achieved encouraging progress in the last couple of years. Novel network architectures have been designed to handle the scale variance issue in crowd images. For this purpose, the ideas of using multi-column networks with different convolution kernel sizes and rich feature fusion have been prevalent in literature. Recent works have shown the effectiveness of \textit{Inception} modules in crowd counting due to its ability to capture multi-scale visual information via the fusion of features from multi-column networks. However, the existing crowd counting networks built with \textit{Inception} modules usually have a small number of layers and only employ the basic type of \textit{Inception} modules. In this paper, we investigate the use of pre-trained \textit{Inception} model for crowd counting. Specifically, we firstly benchmark the baseline \textit{Inception-v3} models on commonly used crowd counting datasets and show its superiority to other existing models. Subsequently, we present a Segmentation Guided Attention Network (SGANet) with the \textit{Inception-v3} as the backbone for crowd counting. We also propose a novel curriculum learning strategy for more efficient training of crowd counting networks. Finally, we conduct thorough experiments to compare the performance of SGANet and other state-of-the-art models. The experimental results validate the effectiveness of the segmentation guided attention layer and the curriculum learning strategy in crowd counting.

Includes members:
  Member "ha ccn hierarchical attention based crowd counting network":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi level bottom top and top bottom feature fusion for crowd counting")
  Member "inverse attention guided deep crowd counting network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "bayesian loss for crowd count estimation with point supervision":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "multi level bottom top and top bottom feature fusion for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "using depth for pixel wise detection of adversarial attacks in crowd counting")
  Member "point in box out beyond counting persons in crowds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "crowd scene analysis by output encoding")
  Member "learning spatial awareness to improve crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "from open set to closed set supervised spatial divide and conquer for object counting")
  Member "counting with focus for free":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "plug and play rescaling based crowd counting in static images")
  Member "improving the learning of multi column convolutional neural network for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "crowd counting via hierarchical scale recalibration 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)