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 "analyzing the variety loss in the context of probabilistic trajectory prediction"):
  Abstract: Trajectory or behavior prediction of traffic agents is an important component of autonomous driving and robot planning in general. It can be framed as a probabilistic future sequence generation problem and recent literature has studied the applicability of generative models in this context. The variety or Minimum over N (MoN) loss, which tries to minimize the error between the ground truth and the closest of N output predictions, has been used in these recent learning models to improve the diversity of predictions. In this work, we present a proof to show that the MoN loss does not lead to the ground truth probability density function, but approximately to its square root instead. We validate this finding with extensive experiments on both simulated toy as well as real world datasets. We also propose multiple solutions to compensate for the dilation to show improvement of log likelihood of the ground truth samples in the corrected probability density function.

Includes members:
  Member "precog prediction conditioned on goals in visual multi agent settings":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "inferring 3d shapes from image collections using adversarial networks":
    also connected through other hyperedges:
      (none)
  Member "generative hybrid representations for activity forecasting with no regret learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dlow diversifying latent flows for diverse human motion prediction")
  Member "what the constant velocity model can teach us about pedestrian motion prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "transformer networks for trajectory forecasting")
  Member "peeking into the future predicting future person activities and locations in videos":
    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 "sophie an attentive gan for predicting paths compliant to social and physical constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans")
  Member "convolutional social pooling for vehicle trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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.LG(Machine Learning)