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: "longitudinal trajectory forecasting of human driven vehicles near traffic lights using vehicle communications"
  Abstract: Forecasting trajectories of human-driven vehicles is a crucial problem in autonomous driving. Trajectory forecasting in the urban area is particularly hard due to complex interactions with actors (cars and pedestrians), and traffic lights (TLs). Unlike the former that has been actively studied, the impact of TLs on the prediction has rarely been discussed. Inspired by the fact that human drives differently depending on phases (green, yellow, red) and timings (elapsed time), we propose a novel approach to the trajectory forecasting problem. In our approach, we take the states of TLs as part of the conditional inputs to our deep-learning models (Human Policy Models) which map a sequence of a vehicle's states and a context to a subsequent action (longitudinal acceleration) of the vehicle. Trained on real-world naturalistic driving data recorded near a signalized intersection over 2 years, the models learn how human drivers react to various states of TLs. These Human Policy Models are then used in trajectory forecasting; the key idea is to utilize the future phases and timings of TLs obtained through vehicle-to-infrastructure communications. Accordingly, an ablation study is presented to show that utilization of the phases and timings of TL significantly improves the accuracy of the forecasts. Finally, our probabilistic Human Policy Models provides probabilistic contexts for the forecasts and captures competing policies, for example, pass or stop in the yellow-light dilemma zone.

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)