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: "trafficpredict trajectory prediction for heterogeneous traffic agents"
  Abstract: To safely and efficiently navigate in complex urban traffic, autonomous vehicles must make responsible predictions in relation to surrounding traffic-agents (vehicles, bicycles, pedestrians, etc.). A challenging and critical task is to explore the movement patterns of different traffic-agents and predict their future trajectories accurately to help the autonomous vehicle make reasonable navigation decision. To solve this problem, we propose a long short-term memory-based (LSTM-based) realtime traffic prediction algorithm, TrafficPredict. Our approach uses an instance layer to learn instances' movements and interactions and has a category layer to learn the similarities of instances belonging to the same type to refine the prediction. In order to evaluate its performance, we collected trajectory datasets in a large city consisting of varying conditions and traffic densities. The dataset includes many challenging scenarios where vehicles, bicycles, and pedestrians move among one another. We evaluate the performance of TrafficPredict on our new dataset and highlight its higher accuracy for trajectory prediction by comparing with prior prediction methods.

Connected through hyperedge HE-1 (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"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "a behavior driven approach for sampling rare event situations for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-2 (induced by source paper "multiple futures prediction"):
  with members:
    - "multipath multiple probabilistic anchor trajectory hypotheses for behavior prediction"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "aads augmented autonomous driving simulation using data driven algorithms"):
  with members:
    - "the apolloscape open dataset for autonomous driving and its application"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "augmented lidar simulator for autonomous driving"
    - "heter sim heterogeneous multi agent systems simulation by interactive data driven optimization"
    - "a lidar point cloud generator from a virtual world to autonomous driving"
    - "sim4cv a photo realistic simulator for computer vision applications"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "carla an open urban driving simulator"

Connected through hyperedge HE-4 (induced by source paper "lane attention predicting vehicles moving trajectories by learning their attention over lanes"):
  with members:
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "an lstm network for highway trajectory prediction"
    - "graph networks as learnable physics engines for inference and control"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "scene lstm a model for human trajectory prediction"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-5 (induced by source paper "drogon a causal reasoning framework for future trajectory forecast"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "looking to relations for future trajectory forecast"
    - "what the constant velocity model can teach us about pedestrian motion prediction"
    - "sophie an attentive gan for predicting paths compliant to social and physical constraints"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "mx lstm mixing tracklets and vislets to jointly forecast trajectories and head poses"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"

Connected through hyperedge HE-6 (induced by source paper "key ingredients of self driving cars"):
  with members:
    - "road crack detection using deep convolutional neural network and adaptive thresholding"
    - "automatic calibration of multiple 3d lidars in urban environments"
    - "using dp towards a shortest path problem related application"
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "a novel dual lidar calibration algorithm using planar surfaces"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision for road surface 3 d reconstruction"
    - "a novel disparity transformation algorithm for road segmentation"

Connected through hyperedge HE-7 (induced by source paper "interaction aware kalman neural networks for trajectory prediction"):
  with members:
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "multi modal trajectory prediction of surrounding vehicles with maneuver based lstms"
    - "baidu apollo em motion planner"
    - "multi agent imitation learning for driving simulation"
    - "imitating driver behavior with generative adversarial networks"
    - "long short term memory kalman filters recurrent neural estimators for pose regularization"

Connected through hyperedge HE-8 (induced by source paper "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"):
  with members:
    - "generic prediction architecture considering both rational and irrational driving behaviors"
    - "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"
    - "a framework for probabilistic generic traffic scene prediction"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "courteous autonomous cars"

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)