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: "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
  Abstract: To safely and efficiently navigate through complex traffic scenarios, autonomous vehicles need to have the ability to predict the future motion of surrounding vehicles. Multiple interacting agents, the multi-modal nature of driver behavior, and the inherent uncertainty involved in the task make motion prediction of surrounding vehicles a challenging problem. In this paper, we present an LSTM model for interaction aware motion prediction of surrounding vehicles on freeways. Our model assigns confidence values to maneuvers being performed by vehicles and outputs a multi-modal distribution over future motion based on them. We compare our approach with the prior art for vehicle motion prediction on the publicly available NGSIM US-101 and I-80 datasets. Our results show an improvement in terms of RMS values of prediction error. We also present an ablative analysis of the components of our proposed model and analyze the predictions made by the model in complex traffic scenarios.

Connected through hyperedge HE-1 (induced by source paper "plop probabilistic polynomial objects trajectory planning for autonomous driving"):
  with members:
    - "trajectory forecasts in unknown environments conditioned on grid based plans"
    - "urban driving with conditional imitation learning"
    - "grip graph based interaction aware trajectory prediction"
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "multiple futures prediction"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "conditional vehicle trajectories prediction in carla urban environment"
    - "on the variance of the adaptive learning rate and beyond"

Connected through hyperedge HE-2 (induced by source paper "survey of deep reinforcement learning for motion planning of autonomous vehicles"):
  with members:
    - "reinforcement learning with probabilistic guarantees for autonomous driving"
    - "cooperation aware reinforcement learning for merging in dense traffic"
    - "lane change decision making through deep reinforcement learning with rule based constraints"
    - "safe efficient and comfortable velocity control based on reinforcement learning for autonomous driving"
    - "autonomous highway driving using deep reinforcement learning"
    - "improved robustness and safety for autonomous vehicle control with adversarial reinforcement learning"
    - "human like autonomous car following model with deep reinforcement learning"
    - "combining planning and deep reinforcement learning in tactical decision making for autonomous driving"

Connected through hyperedge HE-3 (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"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "convolutional social pooling for vehicle trajectory prediction"

Connected through hyperedge HE-4 (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-5 (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"

Connected through hyperedge HE-6 (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"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"

Connected through hyperedge HE-7 (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"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "uncertainty aware short term motion prediction of traffic actors for autonomous driving"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "naturalistic driver intention and path prediction using recurrent neural networks"

Connected through hyperedge HE-8 (induced by source paper "non gaussian chance constrained trajectory planning for autonomous vehicles in the presence of uncertain agents"):
  with members:
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "deep kinematic models for physically realistic prediction of vehicle trajectories"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "provably safe robot navigation with obstacle uncertainty"

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.CV(Computer Vision and Pattern Recognition)