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 "plop probabilistic polynomial objects trajectory planning for autonomous driving"):
  Abstract: To navigate safely in an urban environment, an autonomous vehicle (ego vehicle) needs to understand and anticipate its surroundings, in particular the behavior of other road users (neighbors). However, multiple choices are often acceptable (e.g. turn right or left, or different ways of avoiding an obstacle). We focus here on predicting multiple feasible future trajectories both for the ego vehicle and neighbors through a probabilistic framework. We use a conditional imitation learning algorithm, conditioned by a navigation command for the ego vehicle (e.g. "turn right"). It takes as input the ego car front camera image, a Lidar point cloud in a bird-eye view grid and present and past objects detections to output ego vehicle and neighbors possible trajectories but also semantic segmentation as an auxiliary loss. We evaluate our method on the publicly available dataset nuScenes, showing state-of-the-art performance and investigating the impact of our architecture choices.

Includes members:
  Member "trajectory forecasts in unknown environments conditioned on grid based plans":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "transformer networks for trajectory forecasting")
  Member "urban driving with conditional imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who make drivers stop towards driver centric risk assessment risk object identification via causal inference")
  Member "grip graph based interaction aware trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sequential forecasting of 100 000 points")
  Member "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction":
    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 "multiple futures prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle")
  Member "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "conditional vehicle trajectories prediction in carla urban environment":
    also connected through other hyperedges:
      (none)
  Member "on the variance of the adaptive learning rate and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)