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: "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
  Abstract: Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the livelihood and life quality of many more. However, despite large interest and a number of industry players working in the autonomous domain, there still remains more to be done in order to develop a system capable of operating at a level comparable to best human drivers. One reason for this is high uncertainty of traffic behavior and large number of situations that an SDV may encounter on the roads, making it very difficult to create a fully generalizable system. To ensure safe and efficient operations, an autonomous vehicle is required to account for this uncertainty and to anticipate a multitude of possible behaviors of traffic actors in its surrounding. We address this critical problem and present a method to predict multiple possible trajectories of actors while also estimating their probabilities. The method encodes each actor's surrounding context into a raster image, used as input by deep convolutional networks to automatically derive relevant features for the task. Following extensive offline evaluation and comparison to state-of-the-art baselines, the method was successfully tested on SDVs in closed-course tests.

Connected through hyperedge HE-1 (induced by source paper "trajectory forecasts in unknown environments conditioned on grid based plans"):
  with members:
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "scene compliant trajectory forecast with agent centric spatio temporal grids"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"

Connected through hyperedge HE-2 (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"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "sdvtracker real time multi sensor association and tracking for self driving vehicles"):
  with members:
    - "lasernet an efficient probabilistic 3d object detector for autonomous driving"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "robust multi modality multi object tracking"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "no blind spots full surround multi object tracking for autonomous vehicles using cameras lidars"
    - "online visual robot tracking and identification using deep lstm networks"
    - "focal loss for dense object detection"
    - "what uncertainties do we need in bayesian deep learning for computer vision"

Connected through hyperedge HE-4 (induced by source paper "long term prediction of vehicle behavior using short term uncertainty aware trajectories and high definition maps"):
  with members:
    - "human motion trajectory prediction a survey"
    - "self driving cars a survey"
    - "discrete residual flow for probabilistic pedestrian behavior prediction"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "naturalistic driver intention and path prediction using recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "carpal confidence aware intent recognition for parallel autonomy"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "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-6 (induced by source paper "a review of tracking prediction and decision making methods for autonomous driving"):
  with members:
    - "online multi object tracking with instance aware tracker and dynamic model refreshment"
    - "tracking without bells and whistles"
    - "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models"
    - "online multi object tracking with dual matching attention networks"
    - "multi object tracking with multiple cues and switcher aware classification"
    - "online multi object tracking with historical appearance matching and scene adaptive detection filtering"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "beyond pixels leveraging geometry and shape cues for online multi object tracking"

Connected through hyperedge HE-7 (induced by source paper "cmts conditional multiple trajectory synthesizer for generating safety critical driving scenarios"):
  with members:
    - "fully featured attribute transfer"
    - "a multi domain feature learning method for visual place recognition"
    - "a closed form solution to universal style transfer"
    - "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning"
    - "interpolation in generative models"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"

Connected through hyperedge HE-8 (induced by source paper "mixture dense regression for object detection and human pose estimation"):
  with members:
    - "rethinking on multi stage networks for human pose estimation"
    - "lvis a dataset for large vocabulary instance segmentation"
    - "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"
    - "gaussian yolov3 an accurate and fast object detector using localization uncertainty for autonomous driving"
    - "feature selective anchor free module for single shot object detection"
    - "unsupervised discovery of parts structure and dynamics"
    - "pifpaf composite fields for human pose estimation"
    - "scale aware trident networks for object detection"

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)