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: "identifying driver behaviors using trajectory features for vehicle navigation"
  Abstract: We present a novel approach to automatically identify driver behaviors from vehicle trajectories and use them for safe navigation of autonomous vehicles. We propose a novel set of features that can be easily extracted from car trajectories. We derive a data-driven mapping between these features and six driver behaviors using an elaborate web-based user study. We also compute a summarized score indicating a level of awareness that is needed while driving next to other vehicles. We also incorporate our algorithm into a vehicle navigation simulation system and demonstrate its benefits in terms of safer real-time navigation, while driving next to aggressive or dangerous drivers.

Connected through hyperedge HE-1 (induced by source paper "cmetric a driving behavior measure using centrality functions"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "graphrqi classifying driver behaviors using graph spectrums"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"

Connected through hyperedge HE-2 (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-3 (induced by source paper "graphrqi classifying driver behaviors using graph spectrums"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "self driving cars a survey"
    - "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"
    - "roadtrack tracking road agents in dense and heterogeneous environments"
    - "rules of the road predicting driving behavior with a convolutional model of semantic interactions"
    - "densepeds pedestrian tracking in dense crowds using front rvo and sparse features"
    - "intent aware multi agent reinforcement learning"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"

Connected through hyperedge HE-4 (induced by source paper "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"):
  with members:
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "how would surround vehicles move a unified framework for maneuver classification and motion prediction"
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "short term motion prediction of traffic actors for autonomous driving using deep convolutional networks"
    - "object detection with deep learning a review"
    - "desire distant future prediction in dynamic scenes with interacting agents"

Connected through hyperedge HE-5 (induced by source paper "trafficpredict trajectory prediction for heterogeneous traffic agents"):
  with members:
    - "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"
    - "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions"
    - "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"
    - "attention is all you need"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-6 (induced by source paper "autorvo local navigation with dynamic constraints in dense heterogeneous traffic"):
  with members:
    - "efficient reciprocal collision avoidance between heterogeneous agents using ctmat"
    - "autonomous driving among many pedestrians models and algorithms"
    - "autonovi autonomous vehicle planning with dynamic maneuvers and traffic constraints"
    - "carla an open urban driving simulator"

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)