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: "a framework for probabilistic generic traffic scene prediction"
  Abstract: In a given scenario, simultaneously and accurately predicting every possible interaction of traffic participants is an important capability for autonomous vehicles. The majority of current researches focused on the prediction of an single entity without incorporating the environment information. Although some approaches aimed to predict multiple vehicles, they either predicted each vehicle independently with no considerations on possible interaction with surrounding entities or generated discretized joint motions which cannot be directly used in decision making and motion planning for autonomous vehicle. In this paper, we present a probabilistic framework that is able to jointly predict continuous motions for multiple interacting road participants under any driving scenarios and is capable of forecasting the duration of each interaction, which can enhance the prediction performance and efficiency. The proposed traffic scene prediction framework contains two hierarchical modules: the upper module and the lower module. The upper module forecasts the intention of the predicted vehicle, while the lower module predicts motions for interacting scene entities. An exemplar real-world scenario is used to implement and examine the proposed framework.

Connected through hyperedge HE-1 (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"
    - "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"
    - "courteous autonomous cars"
    - "probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network"

Connected through hyperedge HE-2 (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"
    - "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"
    - "courteous autonomous cars"

Connected through hyperedge HE-3 (induced by source paper "towards a fatality aware benchmark of probabilistic reaction prediction in highly interactive driving scenarios"):
  with members:
    - "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 vehicle semantic intention and motion"
    - "probabilistic prediction of interactive driving behavior via hierarchical inverse reinforcement learning"
    - "accelerated evaluation of automated vehicles safety in lane change scenarios based on importance sampling techniques"

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.LG(Machine Learning)