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 "crowd density forecasting by modeling patch based dynamics"):
  Abstract: Forecasting human activities observed in videos is a long-standing challenge in computer vision, which leads to various real-world applications such as mobile robots, autonomous driving, and assistive systems. In this work, we present a new visual forecasting task called crowd density forecasting. Given a video of a crowd captured by a surveillance camera, our goal is to predict how that crowd will move in future frames. To address this task, we have developed the patch-based density forecasting network (PDFN), which enables forecasting over a sequence of crowd density maps describing how crowded each location is in each video frame. PDFN represents a crowd density map based on spatially overlapping patches and learns density dynamics patch-wise in a compact latent space. This enables us to model diverse and complex crowd density dynamics efficiently, even when the input video involves a variable number of crowds that each move independently. Experimental results with several public datasets demonstrate the effectiveness of our approach compared with state-of-the-art forecasting methods.

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 "sr lstm state refinement for lstm towards pedestrian trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "human motion trajectory prediction a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequential forecasting of 100 000 points")
  Member "looking to relations for future trajectory forecast":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "c 3 framework an open source pytorch code for crowd counting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "nwpu crowd a large scale benchmark for crowd counting")
  Member "multi agent tensor fusion for contextual trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "discrete residual flow for probabilistic pedestrian behavior prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trajectron multi agent generative trajectory forecasting with heterogeneous data for control")
  Member "precog prediction conditioned on goals in visual multi agent settings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "probabilistic future prediction for video scene understanding")

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)