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 "nemo future object localization using noisy ego priors"):
  Abstract: Predictive models for forecasting future behavior of road agents should consider the multi-modal nature and be aware of the uncertainty of their predictions. Particularly from the egocentric view where the motion of other agents is captured with respect to the ego-motion, the uncertainty of ego-motion prediction is critical to determine their interactive reactions and behaviors. Along this line, we propose NEMO (Noisy Ego MOtion priors for future object localization) for future forecast of road agents in the egocentric view. A predictive distribution of future forecast is jointly modeled with the uncertainty of predictions. For this, we divide the problem into two tasks: future ego-motion prediction and future object localization. We first model the multi-modal distribution of future ego-motion with uncertainty estimates. The resulting distribution of ego-behavior is used to sample multiple modes of future ego-motion. Then, each modality is used as a prior to understand the interactions between the ego-vehicle and target agent. We predict the multi-modal future locations of the target from individual modes of the ego-vehicle, modeling the uncertainty of target's behavior. To this end, we extensively evaluate the proposed framework using the publicly available benchmark dataset (HEV-I) with an addition of Inertial Measurement Unit (IMU) data to it.

Includes members:
  Member "drogon a causal reasoning framework for future trajectory forecast":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle")
  Member "looking to relations for future trajectory forecast":
    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 "the h3d dataset for full surround 3d multi object detection and tracking in crowded urban scenes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "3d object detection from lidar data using distance depended feature extraction")
  Member "what the constant velocity model can teach us about pedestrian motion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transformer networks for trajectory forecasting")
  Member "egocentric vision based future vehicle localization for intelligent driving assistance systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction")
  Member "social gan socially acceptable trajectories with generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "convolutional neural network for trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "graphtcn spatio temporal interaction modeling for human trajectory prediction")
  Member "joint person segmentation and identification in synchronized first and third person videos":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis")

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)