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 "generating high quality crowd density maps using contextual pyramid cnns"):
  Abstract: We present a novel method called Contextual Pyramid CNN (CP-CNN) for generating high-quality crowd density and count estimation by explicitly incorporating global and local contextual information of crowd images. The proposed CP-CNN consists of four modules: Global Context Estimator (GCE), Local Context Estimator (LCE), Density Map Estimator (DME) and a Fusion-CNN (F-CNN). GCE is a VGG-16 based CNN that encodes global context and it is trained to classify input images into different density classes, whereas LCE is another CNN that encodes local context information and it is trained to perform patch-wise classification of input images into different density classes. DME is a multi-column architecture-based CNN that aims to generate high-dimensional feature maps from the input image which are fused with the contextual information estimated by GCE and LCE using F-CNN. To generate high resolution and high-quality density maps, F-CNN uses a set of convolutional and fractionally-strided convolutional layers and it is trained along with the DME in an end-to-end fashion using a combination of adversarial loss and pixel-level Euclidean loss. Extensive experiments on highly challenging datasets show that the proposed method achieves significant improvements over the state-of-the-art methods.

Includes members:
  Member "switching convolutional neural network for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "spatiotemporal modeling for crowd counting in videos":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "understanding traffic density from large scale web camera data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi source domain adaptation in the deep learning era a systematic survey")
  Member "multi style generative network for real time transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "domain adaptive few shot learning")
  Member "fcn rlstm deep spatio temporal neural networks for vehicle counting in city cameras":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dual convolutional lstm network for referring image segmentation")
  Member "a survey of recent advances in cnn based single image crowd counting and density estimation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "resnetcrowd a residual deep learning architecture for crowd counting violent behaviour detection and crowd density level classification":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")

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)