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 "nwpu crowd a large scale benchmark for crowd counting"):
  Abstract: In the last decade, crowd counting attracts much attention of researchers due to its wide-spread applications, including crowd monitoring, public safety, space design, etc. Many Convolutional Neural Networks (CNN) are designed for tackling this task. However, currently released datasets are so small-scale that they can not meet the needs of the supervised CNN-based algorithms. To remedy this problem, we construct a large-scale congested crowd counting dataset, NWPU-Crowd, consisting of $5,109$ images, in a total of $2,133,238$ annotated heads. Compared with other real-world datasets, it contains various illumination scenes and has the largest density range ($0\!\sim\!20,033$). Besides, a benchmark website is developed for impartially evaluating the different methods, which allows researchers to submit the results of the test set. Based on the proposed dataset, we further describe the data characteristics, evaluate the performance of some mainstream state-of-the-art (SOTA) methods, and analyze the new problems that arise on the new data. What's more, NWPU-Crowd Dataset is available at \url{this http URL}, and the code is open-sourced at \url{this https URL}

Includes members:
  Member "scar spatial channel wise attention regression networks for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "focus on semantic consistency for cross domain crowd understanding")
  Member "drone based joint density map estimation localization and tracking with space time multi scale attention network":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "era a dataset and deep learning benchmark for event recognition in aerial videos")
  Member "exploiting unlabeled data in cnns by self supervised learning to rank":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "self supervised contextual bandits in computer vision")
  Member "locality constrained spatial transformer network for video crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "towards using count level weak supervision for crowd counting")
  Member "bayesian loss for crowd count estimation with point supervision":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "towards using count level weak supervision for crowd counting")
  Member "c 3 framework an open source pytorch code for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "encoder decoder based convolutional neural networks with multi scale aware modules for crowd counting")
  Member "the apolloscape open dataset for autonomous driving and its application":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "weighted average precision adversarial example detection in the visual perception of autonomous vehicles")
  Member "perspective guided convolution networks for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "drone based rgbt vehicle detection and counting a challenge")

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)