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 "cnn based cascaded multi task learning of high level prior and density estimation for crowd counting"):
  Abstract: Estimating crowd count in densely crowded scenes is an extremely challenging task due to non-uniform scale variations. In this paper, we propose a novel end-to-end cascaded network of CNNs to jointly learn crowd count classification and density map estimation. Classifying crowd count into various groups is tantamount to coarsely estimating the total count in the image thereby incorporating a high-level prior into the density estimation network. This enables the layers in the network to learn globally relevant discriminative features which aid in estimating highly refined density maps with lower count error. The joint training is performed in an end-to-end fashion. Extensive experiments on highly challenging publicly available datasets show that the proposed method achieves lower count error and better quality density maps as compared to the recent state-of-the-art methods.

Includes members:
  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-1 (induced by source paper "generative models for novelty detection applications in abnormal event and situational change detection from data series")
  Member "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "two stream aural visual affect analysis in the wild")
  Member "crowdnet a deep convolutional network for dense crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "fully convolutional crowd counting on highly congested scenes":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust regression via deep negative correlation learning")
  Member "instance aware semantic segmentation via multi task network cascades":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi channel attention selection gans for guided image to image translation")
  Member "crowded scene analysis a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "people counting in high density crowds from still images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "encoder decoder based convolutional neural networks with multi scale aware modules for crowd counting")
  Member "spatial pyramid pooling in deep convolutional networks for visual recognition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

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)