human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "graphrqi classifying driver behaviors using graph spectrums"):
  Abstract: We present a novel algorithm (GraphRQI) to identify driver behaviors from road-agent trajectories. Our approach assumes that the road-agents exhibit a range of driving traits, such as aggressive or conservative driving. Moreover, these traits affect the trajectories of nearby road-agents as well as the interactions between road-agents. We represent these inter-agent interactions using unweighted and undirected traffic graphs. Our algorithm classifies the driver behavior using a supervised learning algorithm by reducing the computation to the spectral analysis of the traffic graph. Moreover, we present a novel eigenvalue algorithm to compute the spectrum efficiently. We provide theoretical guarantees for the running time complexity of our eigenvalue algorithm and show that it is faster than previous methods by 2 times. We evaluate the classification accuracy of our approach on traffic videos and autonomous driving datasets corresponding to urban traffic. In practice, GraphRQI achieves an accuracy improvement of up to 25% over prior driver behavior classification algorithms. We also use our classification algorithm to predict the future trajectories of road-agents.

Includes members:
  Member "behavior planning of autonomous cars with social perception":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cmetric a driving behavior measure using centrality functions")
  Member "self driving cars a survey":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "3d point cloud processing and learning for autonomous driving")
  Member "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "joint 3d tracking and forecasting with graph neural network and diversity sampling")
  Member "roadtrack tracking road agents in dense and heterogeneous environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cmetric a driving behavior measure using centrality functions")
  Member "rules of the road predicting driving behavior with a convolutional model of semantic interactions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")
  Member "densepeds pedestrian tracking in dense crowds using front rvo and sparse features":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "cmetric a driving behavior measure using centrality functions")
  Member "intent aware multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cmetric a driving behavior measure using centrality functions")
  Member "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequential forecasting of 100 000 points")

Question: Please tell me which class the source paper 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)