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: "merging in congested freeway traffic using multipolicy decision making and passive actor critic learning"
  Abstract: Merging in congested freeway traffic is a significant challenge toward realizing fully automated (level 4) driving. Merging vehicles need to decide not only how to merge safely into a spot, but also where to merge. We present a method for freeway merge based on multipolicy decision making coupled with a reinforcement learning technique called passive actor-critic (pAC), which learns with less knowledge of the system and without active exploration. The multipolicy decision making selects a candidate spot for merging by using the state value learned by pAC. Together, these techniques yield a method that first decides where to merge and then realizes safe merging. We evaluate our method using real traffic data. Our experiments show that pAC achieves an overall success rate of 92% for merging into a predetermined spot on a freeway, which is comparable to human decision making.

Connected through hyperedge HE-1 (induced by source paper "sa net deep neural network for robot trajectory recognition from rgb d streams"):
  with members:
    - "yolov3 an incremental improvement"
    - "scene flow to action map a new representation for rgb d based action recognition with convolutional neural networks"
    - "temporal activity detection in untrimmed videos with recurrent neural networks"
    - "beyond short snippets deep networks for video classification"
    - "deep residual learning for image recognition"
    - "maximum entropy deep inverse reinforcement learning"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "multimodal deep learning for robust rgb d object recognition"

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.AI(Artificial Intelligence)