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: "uncertainty aware driver trajectory prediction at urban intersections"
  Abstract: Predicting the motion of a driver's vehicle is crucial for advanced driving systems, enabling detection of potential risks towards shared control between the driver and automation systems. In this paper, we propose a variational neural network approach that predicts future driver trajectory distributions for the vehicle based on multiple sensors. Our predictor generates both a conditional variational distribution of future trajectories, as well as a confidence estimate for different time horizons. Our approach allows us to handle inherently uncertain situations, and reason about information gain from each input, as well as combine our model with additional predictors, creating a mixture of experts. We show how to augment the variational predictor with a physics-based predictor, and based on their confidence estimations, improve overall system performance. The resulting combined model is aware of the uncertainty associated with its predictions, which can help the vehicle autonomy to make decisions with more confidence. The model is validated on real-world urban driving data collected in multiple locations. This validation demonstrates that our approach improves the prediction error of a physics-based model by 25% while successfully identifying the uncertain cases with 82% accuracy.

Connected through hyperedge HE-1 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network"):
  with members:
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "human motion trajectory prediction a survey"
    - "looking to relations for future trajectory forecast"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "potential field interpretable and unified representation for trajectory prediction"
    - "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"

Connected through hyperedge HE-2 (induced by source paper "carpal confidence aware intent recognition for parallel autonomy"):
  with members:
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "the trajectron probabilistic multi agent trajectory modeling with dynamic spatiotemporal graphs"
    - "safe end to end imitation learning for model predictive control"
    - "probabilistically safe robot planning with confidence based human predictions"
    - "shared autonomy via deep reinforcement learning"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-3 (induced by source paper "disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision"):
  with members:
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "openpose realtime multi person 2d pose estimation using part affinity fields"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "convolutional sequence to sequence model for human dynamics"
    - "action agnostic human pose forecasting"
    - "bdd100k a diverse driving video database with scalable annotation tooling"
    - "deep autoencoder for combined human pose estimation and body model upscaling"

Connected through hyperedge HE-4 (induced by source paper "drogon a causal reasoning framework for future trajectory forecast"):
  with members:
    - "looking to relations for future trajectory forecast"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"

Connected through hyperedge HE-5 (induced by source paper "diversity aware vehicle motion prediction via latent semantic sampling"):
  with members:
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "analyzing the variety loss in the context of probabilistic trajectory 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"

Connected through hyperedge HE-6 (induced by source paper "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"):
  with members:
    - "recurrent neural processes"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "attentive neural processes"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "the functional neural process"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "neural processes"
    - "a new multi vehicle trajectory generator to simulate vehicle to vehicle encounters"

Connected through hyperedge HE-7 (induced by source paper "uncertainty quantification with statistical guarantees in end to end autonomous driving control"):
  with members:
    - "statistical guarantees for the robustness of bayesian neural networks"
    - "central limit model checking"
    - "ensemble bayesian decision making with redundant deep perceptual control policies"
    - "robustness guarantees for bayesian inference with gaussian processes"
    - "towards safe autonomous driving capture uncertainty in the deep neural network for lidar 3d vehicle detection"
    - "carla an open urban driving simulator"
    - "explaining how a deep neural network trained with end to end learning steers a car"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-8 (induced by source paper "risk aware reasoning for autonomous vehicles"):
  with members:
    - "predicting driver attention in critical situations"
    - "computer vision for autonomous vehicles problems datasets and state of the art"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "training a network to attend like human drivers saves it from common but misleading loss functions"
    - "the social dilemma of autonomous vehicles"
    - "shallow and deep convolutional networks for saliency prediction"
    - "instance level segmentation for autonomous driving with deep densely connected mrfs"
    - "decentralized control of partially observable markov decision processes using belief space macro actions"

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.RO(Robotics)