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: "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
  Abstract: Despite its ubiquity in our daily lives, AI is only just starting to make advances in what may arguably have the largest societal impact thus far, the nascent field of autonomous driving. In this work we discuss this important topic and address one of crucial aspects of the emerging area, the problem of predicting future state of autonomous vehicle's surrounding necessary for safe and efficient operations. We introduce a deep learning-based approach that takes into account current world state and produces rasterized representations of each actor's vicinity. The raster images are then used by deep convolutional models to infer future movement of actors while accounting for inherent uncertainty of the prediction task. Extensive experiments on real-world data strongly suggest benefits of the proposed approach. Moreover, following successful tests the system was deployed to a fleet of autonomous vehicles.

Connected through hyperedge HE-1 (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-2 (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-3 (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-4 (induced by source paper "learning a directional soft lane affordance model for road scenes using self supervision"):
  with members:
    - "self driving cars a survey"
    - "learning human aware path planning with fully convolutional networks"
    - "graph r cnn for scene graph generation"
    - "learning long range perception using self supervision from short range sensors and odometry"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "a parametric top view representation of complex road scenes"
    - "pixels to graphs by associative embedding"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-5 (induced by source paper "scale net scalable vehicle trajectory prediction network under random number of interacting vehicles via edge enhanced graph convolutional neural network"):
  with members:
    - "graph neural networks for modelling traffic participant interaction"
    - "interaction aware kalman neural networks for trajectory prediction"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "exploiting edge features in graph neural networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"

Connected through hyperedge HE-6 (induced by source paper "perception as prediction using general value functions in autonomous driving applications"):
  with members:
    - "composable action conditioned predictors flexible off policy learning for robot navigation"
    - "learning to act by predicting the future"
    - "multi timescale nexting in a reinforcement learning robot"

Connected through hyperedge HE-7 (induced by source paper "depth wise decomposition for accelerating separable convolutions in efficient convolutional neural networks"):
  with members:
    - "mobinet a mobile binary network for image classification"
    - "pack and detect fast object detection in videos using region of interest packing"
    - "feature selective anchor free module for single shot object detection"
    - "prediction tracking segmentation"
    - "amc automl for model compression and acceleration on mobile devices"
    - "bounding box regression with uncertainty for accurate object detection"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "haq hardware aware automated quantization with mixed precision"

Connected through hyperedge HE-8 (induced by source paper "exploring applications of deep reinforcement learning for real world autonomous driving systems"):
  with members:
    - "on first order meta learning algorithms"
    - "simple random search provides a competitive approach to reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "large scale study of curiosity driven learning"
    - "world models"
    - "a reinforcement learning based approach for automated lane change maneuvers"
    - "hierarchical imitation and reinforcement learning"
    - "learning to drive in a day"

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)