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 "estimating people flows to better count them in crowded scenes"):
  Abstract: State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate people densities in individual images. As such, only very few take advantage of temporal consistency in video sequences, and those that do only impose weak smoothness constraints across consecutive frames. #R##N#In this paper, we show that estimating people flows across image locations between consecutive images and inferring the people densities from these flows instead of directly regressing them makes it possible to impose much stronger constraints encoding the conservation of the number of people, which significantly boost performance without requiring a more complex architecture. Furthermore, it also enables us to exploit the correlation between people flow and optical flow to further improve the results. #R##N#We will demonstrate that we consistently outperform state-of-the-art methods on five benchmark datasets.

Includes members:
  Member "locality constrained spatial transformer network for video crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "bayesian loss for crowd count estimation with point supervision":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (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-4 (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-5 (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-6 (induced by source paper "plug and play rescaling based crowd counting in static images")
  Member "learn to scale generating multipolar normalized density map for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting")
  Member "perspective guided convolution networks for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")

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)