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 "joint 3d tracking and forecasting with graph neural network and diversity sampling"):
  Abstract: 3D multi-object tracking (MOT) and trajectory forecasting are two critical components in modern 3D perception systems that require accurate modeling of multi-agent interaction. We hypothesize that it is beneficial to unify both tasks under one framework in order to learn a shared feature representation of agent interaction. To evaluate this hypothesis, we propose a unified solution for 3D MOT and trajectory forecasting which also incorporates two additional novel computational units. First, we propose a feature interaction technique by introducing Graph Neural Networks (GNNs) to capture the way in which multiple agents interact with one another. The GNN is able to model complex hierarchical interactions, improve the discriminative feature learning for MOT association, and provide socially-aware context for trajectory forecasting. Second, we use a diversity sampling function to improve the quality and diversity of our forecasted trajectories. The learned sampling function is trained to efficiently extract a variety of outcomes from a generative trajectory distribution and helps avoid the problem of generating many duplicate trajectory samples. We evaluate on the KITTI and nuScenes datasets, showing that our unified method with feature interaction and diversity sampling achieves new state-of-the-art performance on both 3D MOT and trajectory forecasting. Our code will be made available at this https URL.

Includes members:
  Member "complexer yolo real time 3d object detection and tracking on semantic point clouds":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d object detection from lidar data using distance depended feature extraction")
  Member "grip graph based interaction aware trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sequential forecasting of 100 000 points")
  Member "lagging inference networks and posterior collapse in variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "actional structural graph convolutional networks for skeleton based action recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition")
  Member "dual graph convolutional network for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  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 "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving")
  Member "a baseline for 3d multi object tracking":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequential forecasting of 100 000 points")

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)