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 "scene compliant trajectory forecast with agent centric spatio temporal grids"):
  Abstract: Forecasting long-term human motion is a challenging task due to the non-linearity, multi-modality and inherent uncertainty in future trajectories. The underlying scene and past motion of agents can provide useful cues to predict their future motion. However, the heterogeneity of the two inputs poses a challenge for learning a joint representation of the scene and past trajectories. To address this challenge, we propose a model based on grid representations to forecast agent trajectories. We represent the past trajectories of agents using binary 2-D grids, and the underlying scene as a RGB birds-eye view (BEV) image, with an agent-centric frame of reference. We encode the scene and past trajectories using convolutional layers and generate trajectory forecasts using a Convolutional LSTM (ConvLSTM) decoder. Results on the publicly available Stanford Drone Dataset (SDD) show that our model outperforms prior approaches and outputs realistic future trajectories that comply with scene structure and past motion.

Includes members:
  Member "scene induced multi modal trajectory forecasting via planning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tppo a novel trajectory predictor with pseudo oracle")
  Member "social ways learning multi modal distributions of pedestrian trajectories with gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  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 "predicting motion of vulnerable road users using high definition maps and efficient convnets":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors")
  Member "understanding pedestrian vehicle interactions with vehicle mounted vision an lstm model and empirical analysis":
    also connected through other hyperedges:
      (none)
  Member "peeking into the future predicting future person activities and locations in videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "sophie an attentive gan for predicting paths compliant to social and physical constraints":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "spatiotemporal relationship reasoning for pedestrian intent prediction")
  Member "multimodal trajectory predictions for autonomous driving using deep convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")

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)