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 "cmetric a driving behavior measure using centrality functions"):
  Abstract: We present a new measure, CMetric, to classify driver behaviors using centrality functions. Our formulation combines concepts from computational graph theory and social traffic psychology to quantify and classify the behavior of human drivers. CMetric is used to compute the probability of a vehicle executing a driving style, as well as the intensity used to execute the style. Our approach is designed for realtime autonomous driving applications, where the trajectory of each vehicle or road-agent is extracted from a video. We compute a dynamic geometric graph (DGG) based on the positions and proximity of the road-agents and centrality functions corresponding to closeness and degree. These functions are used to compute the CMetric based on style likelihood and style intensity estimates. Our approach is general and makes no assumption about traffic density, heterogeneity, or how driving behaviors change over time. We present efficient techniques to compute CMetric and demonstrate its performance on well-known autonomous driving datasets. We evaluate the accuracy of CMetric and compare with ground truth behavior labels and with that of a human observer by performing a user study over over a long vehicle trajectory.

Includes members:
  Member "behavior planning of autonomous cars with social perception":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tackling existence probabilities of objects with motion planning for automated urban driving")
  Member "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "graphrqi classifying driver behaviors using graph spectrums")
  Member "densepeds pedestrian tracking in dense crowds using front rvo and sparse features":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "frozone freezing free pedestrian friendly navigation in human crowds")
  Member "graphrqi classifying driver behaviors using graph spectrums":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms")
  Member "intent aware multi agent reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning 3d aware egocentric spatial temporal interaction via graph convolutional networks")
  Member "traphic trajectory prediction in dense and heterogeneous traffic using weighted interactions":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sequential forecasting of 100 000 points")
  Member "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")

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)