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 "a novel graph based trajectory predictor with pseudo oracle"):
  Abstract: Pedestrian trajectory prediction in dynamic scenes remains a challenging and critical problem in numerous applications, such as self-driving cars and socially aware robots. Challenges concentrate on capturing pedestrians' social interactions and handling their future uncertainties. Pedestrians' head orientations can be used as an oracle that indicates relevant pedestrians[1], thus is beneficial to model social interactions. Moreover, latent variable distributions of pedestrians'future trajectories can be termed as another oracle. However, few works fully utilize these oracle information for an improved prediction performance. In this work, we propose GTPPO (Graph-based Trajectory Predictor with Pseudo Oracle), which is a generative model-based trajectory predictor. Pedestrians'social interactions are captured by the proposed GA2T (Graph Attention social Attention neTwork) module. Social attention is calculated on the basis of pedestrians' moving directions, which are termed as a pseudo oracle. Moreover, we propose a latent variable predictor to learn the latent variable distribution from observed trajectories. Such latent variable distribution reflects pedestrians'future trajectories, and therefore can be taken as another pseudo oracle. We compare the performance of GTPPO with several recently proposed methods on benchmarking datasets. Quantitative evaluations demonstrate that GTPPO outperforms state-of-the-art methods with lower average and final displacement errors. Qualitative evaluations show that GTPPO successfully recognizes the sudden motion changes since the estimated latent variable reflects the future trajectories.

Includes members:
  Member "situation aware pedestrian trajectory prediction with spatio temporal attention model":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "effects of a social force model reward in robot navigation based on deep reinforcement learning")
  Member "drogon a causal reasoning framework for future trajectory forecast":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nemo future object localization using noisy ego priors")
  Member "social and scene aware trajectory prediction in crowded spaces":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tppo a novel trajectory predictor with pseudo oracle")
  Member "sr lstm state refinement for lstm towards pedestrian trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "motionnet joint perception and motion prediction for autonomous driving based on bird s eye view maps")
  Member "social ways learning multi modal distributions of pedestrian trajectories with gans":
    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 "diverse trajectory forecasting with determinantal point processes":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "multiple futures prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "analyzing the variety loss in the context of probabilistic trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "graphtcn spatio temporal interaction modeling for human trajectory prediction")

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)