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: "courteous autonomous cars"
  Abstract: Typically, autonomous cars optimize for a combination of safety, efficiency, and driving quality. But as we get better at this optimization, we start seeing behavior go from too conservative to too aggressive. The car's behavior exposes the incentives we provide in its cost function. In this work, we argue for cars that are not optimizing a purely selfish cost, but also try to be courteous to other interactive drivers. We formalize courtesy as a term in the objective that measures the increase in another driver's cost induced by the autonomous car's behavior. Such a courtesy term enables the robot car to be aware of possible irrationality of the human behavior, and plan accordingly. We analyze the effect of courtesy in a variety of scenarios. We find, for example, that courteous robot cars leave more space when merging in front of a human driver. Moreover, we find that such a courtesy term can help explain real human driver behavior on the NGSIM dataset.

Connected through hyperedge HE-1 (induced by source paper "interaction dataset an international adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps"):
  with members:
    - "generic prediction architecture considering both rational and irrational driving behaviors"
    - "human motion trajectory prediction a survey"
    - "top view trajectories a pedestrian dataset of vehicle crowd interaction from controlled experiments and crowded campus"
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "an lstm network for highway trajectory prediction"
    - "deep imitative models for flexible inference planning and control"
    - "discriminative correlation filter tracker with channel and spatial reliability"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"

Connected through hyperedge HE-2 (induced by source paper "multi modal probabilistic prediction of interactive behavior via an interpretable model"):
  with members:
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "sequence to sequence prediction of vehicle trajectory via lstm encoder decoder architecture"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"

Connected through hyperedge HE-3 (induced by source paper "generic prediction architecture considering both rational and irrational driving behaviors"):
  with members:
    - "behavior planning of autonomous cars with social perception"
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "a framework for probabilistic generic traffic scene prediction"
    - "convolutional social pooling for vehicle trajectory prediction"
    - "probabilistic prediction of vehicle semantic intention and motion"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "accurate and diverse sampling of sequences based on a best of many sample objective"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-4 (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"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "behavior planning of autonomous cars with social perception"):
  with members:
    - "people as sensors imputing maps from human actions"
    - "occlusion aware risk assessment for autonomous driving in urban environments"
    - "tackling occlusions limited sensor range with set based safety verification"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "social cohesion in autonomous driving"
    - "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-6 (induced by source paper "towards better human robot collaboration with robust plan recognition and trajectory prediction"):
  with members:
    - "interpretable modelling of driving behaviors in interactive driving scenarios based on cumulative prospect theory"
    - "human motion prediction using semi adaptable neural networks"
    - "human activity recognition in rgb d videos by dynamic images"
    - "goal inference improves objective and perceived performance in human robot collaboration"
    - "robot safe interaction system for intelligent industrial co robots"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "learning human motion models for long term predictions"

Connected through hyperedge HE-7 (induced by source paper "coordination and trajectory prediction for vehicle interactions via bayesian generative modeling"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning"
    - "generic probabilistic interactive situation recognition and prediction from virtual to real"
    - "generic vehicle tracking framework capable of handling occlusions based on modified mixture particle filter"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "bayesian conditional generative adverserial networks"
    - "conditional generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "how shall i drive interaction modeling and motion planning towards empathetic and socially graceful driving"):
  with members:
    - "learning and inferring a driver s braking action in car following scenarios"
    - "learning with opponent learning awareness"
    - "game theoretic modeling of driver and vehicle interactions for verification and validation of autonomous vehicle control systems"

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)