human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "a novel graph based trajectory predictor with pseudo oracle"):
  with members:
    - "situation aware pedestrian trajectory prediction with spatio temporal attention model"
    - "drogon a causal reasoning framework for future trajectory forecast"
    - "social and scene aware trajectory prediction in crowded spaces"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "diverse trajectory forecasting with determinantal point processes"
    - "multiple futures prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"

Connected through hyperedge HE-2 (induced by source paper "graphtcn spatio temporal interaction modeling for human trajectory prediction"):
  with members:
    - "an attention enhanced graph convolutional lstm network for skeleton based action recognition"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "convolutional neural network for trajectory prediction"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "soft hardwired attention an lstm framework for human trajectory prediction and abnormal event detection"
    - "social attention modeling attention in human crowds"

Connected through hyperedge HE-3 (induced by source paper "trajectron multi agent generative trajectory forecasting with heterogeneous data for control"):
  with members:
    - "discrete residual flow for probabilistic pedestrian behavior prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "class balanced grouping and sampling for point cloud 3d object detection"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "the trajectron probabilistic multi agent trajectory modeling with dynamic spatiotemporal graphs"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"

Connected through hyperedge HE-4 (induced by source paper "a spatial temporal attentive network with spatial continuity for trajectory prediction"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "peeking into the future predicting future person activities and locations in videos"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "the trajectron probabilistic multi agent trajectory modeling with dynamic spatiotemporal graphs"

Connected through hyperedge HE-5 (induced by source paper "the garden of forking paths towards multi future trajectory prediction"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "precog prediction conditioned on goals in visual multi agent settings"

Connected through hyperedge HE-6 (induced by source paper "diversity aware vehicle motion prediction via latent semantic sampling"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "efficient autonomy validation in simulation with adaptive stress testing"
    - "disentangled relational representations for explaining and learning from demonstration"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "interpreting the latent space of gans for semantic face editing"
    - "conditional generative neural system for probabilistic trajectory prediction"

Question: Please tell me which class the center node 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)