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: "social attention modeling attention in human crowds"
  Abstract: Robots that navigate through human crowds need to be able to plan safe, efficient, and human predictable trajectories. This is a particularly challenging problem as it requires the robot to predict future human trajectories within a crowd where everyone implicitly cooperates with each other to avoid collisions. Previous approaches to human trajectory prediction have modeled the interactions between humans as a function of proximity. However, that is not necessarily true as some people in our immediate vicinity moving in the same direction might not be as important as other people that are further away, but that might collide with us in the future. In this work, we propose Social Attention, a novel trajectory prediction model that captures the relative importance of each person when navigating in the crowd, irrespective of their proximity. We demonstrate the performance of our method against a state-of-the-art approach on two publicly available crowd datasets and analyze the trained attention model to gain a better understanding of which surrounding agents humans attend to, when navigating in a crowd.

Connected through hyperedge HE-1 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "laserflow efficient and probabilistic object detection and motion forecasting"):
  with members:
    - "std sparse to dense 3d object detector for point cloud"
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "sensor fusion for joint 3d object detection and semantic segmentation"
    - "spatially aware graph neural networks for relational behavior forecasting from sensor data"
    - "pixor real time 3d object detection from point clouds"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "learning an uncertainty aware object detector for autonomous driving"

Connected through hyperedge HE-3 (induced by source paper "transformer networks for trajectory forecasting"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "trajectron multi agent generative trajectory forecasting with heterogeneous data for control"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "human motion trajectory prediction a survey"
    - "safecritic collision aware trajectory prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "generating long sequences with sparse transformers"
    - "social bigat multimodal trajectory forecasting using bicycle gan and graph attention networks"

Connected through hyperedge HE-4 (induced by source paper "diverse and admissible trajectory forecasting through multimodal context understanding"):
  with members:
    - "human motion trajectory prediction a survey"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "collaborative motion prediction via neural motion message passing"):
  with members:
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "graphtcn spatio temporal interaction modeling for human trajectory prediction"):
  with members:
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "visionnet a drivable space based interactive motion prediction network for autonomous driving"):
  with members:
    - "video generation from single semantic label map"
    - "social ways learning multi modal distributions of pedestrian trajectories with gans"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "infer intermediate representations for future prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "starnet pedestrian trajectory prediction using deep neural network in star topology"
    - "peeking into the future predicting future person activities and locations in videos"
    - "video to video synthesis"

Connected through hyperedge HE-8 (induced by source paper "trajectron multi agent generative trajectory forecasting with heterogeneous data for control"):
  with members:
    - "analyzing the variety loss in the context of probabilistic trajectory prediction"
    - "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"

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)