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: "predicting motion of vulnerable road users using high definition maps and efficient convnets"
  Abstract: Following detection and tracking of traffic actors, prediction of their future motion is the next critical component of a self-driving vehicle (SDV) technology, allowing the SDV to operate safely and efficiently in its environment. This is particularly important when it comes to vulnerable road users (VRUs), such as pedestrians and bicyclists. These actors need to be handled with special care due to an increased risk of injury, as well as the fact that their behavior is less predictable than that of motorized actors. To address this issue, in this paper we present a deep learning-based method for predicting VRU movement, where we rasterize high-definition maps and actor's surroundings into bird's-eye view image used as an input to deep convolutional networks. In addition, we propose a fast architecture suitable for real-time inference, and present a detailed ablation study of various rasterization choices. The results strongly indicate benefits of using the proposed approach for motion prediction of VRUs, both in terms of accuracy and latency.

Connected through hyperedge HE-1 (induced by source paper "densecavoid real time navigation in dense crowds using anticipatory behaviors"):
  with members:
    - "yolov3 an incremental improvement"
    - "crowd robot interaction crowd aware robot navigation with attention based deep reinforcement learning"
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "getting robots unfrozen and unlost in dense pedestrian crowds"
    - "motion planning among dynamic decision making agents with deep reinforcement learning"
    - "fully distributed multi robot collision avoidance via deep reinforcement learning for safe and efficient navigation in complex scenarios"
    - "towards monocular vision based obstacle avoidance through deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "collaborative motion prediction via neural motion message passing"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"
    - "deepgcns can gcns go as deep as cnns"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "real world anomaly detection in surveillance videos"
    - "linknet relational embedding for scene graph"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-3 (induced by source paper "efficient latent representations using multiple tasks for autonomous driving"):
  with members:
    - "urban driving with conditional imitation learning"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety"
    - "deep kinematic models for physically realistic prediction of vehicle trajectories"
    - "model free deep reinforcement learning for urban autonomous driving"
    - "covernet multimodal behavior prediction using trajectory sets"
    - "pytorch an imperative style high performance deep learning library"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "spatially aware graph neural networks for relational behavior forecasting from sensor data"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "pixor real time 3d object detection from point clouds"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "relational action forecasting"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-5 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "self driving cars a survey"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "a behavior driven approach for sampling rare event situations for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "graphrqi classifying driver behaviors using graph spectrums"

Connected through hyperedge HE-6 (induced by source paper "scene compliant trajectory forecast with agent centric spatio temporal grids"):
  with members:
    - "scene induced multi modal trajectory forecasting via planning"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "human motion trajectory prediction a survey"
    - "understanding pedestrian vehicle interactions with vehicle mounted vision an lstm model and empirical analysis"
    - "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"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-7 (induced by source paper "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"):
  with members:
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "object detection with deep learning a review"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-8 (induced by source paper "deep kinematic models for physically realistic prediction of vehicle trajectories"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "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"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "social gan socially acceptable trajectories with generative adversarial networks"

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)