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 new multi vehicle trajectory generator to simulate vehicle to vehicle encounters"
  Abstract: Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing. This paper introduces a multi-vehicle trajectory generator (MTG) that can encode multi-vehicle interaction scenarios (called driving encounters) into an interpretable representation from which new driving encounter scenarios are generated by sampling. The MTG consists of a bi-directional encoder and a multi-branch decoder. A new disentanglement metric is then developed for model analyses and comparisons in terms of model robustness and the independence of the latent codes. Comparison of our proposed MTG with $\beta$-VAE and InfoGAN demonstrates that the MTG has stronger capability to purposely generate rational vehicle-to-vehicle encounters through operating the disentangled latent codes. Thus the MTG could provide more data for engineers and researchers to develop testing and evaluation scenarios for autonomous vehicles.

Connected through hyperedge HE-1 (induced by source paper "learning to collide an adaptive safety critical scenarios generating method"):
  with members:
    - "generative adversarial minority oversampling"
    - "active domain randomization"
    - "adversarial autoaugment"
    - "deceptionnet network driven domain randomization"
    - "meta sim learning to generate synthetic datasets"
    - "class balanced loss based on effective number of samples"
    - "fast autoaugment"
    - "cmts conditional multiple trajectory synthesizer for generating safety critical driving scenarios"

Connected through hyperedge HE-2 (induced by source paper "probabilistic trajectory prediction for autonomous vehicles with attentive recurrent neural process"):
  with members:
    - "uncertainty aware driver trajectory prediction at urban intersections"
    - "recurrent neural processes"
    - "predicting vehicle behaviors over an extended horizon using behavior interaction network"
    - "attentive neural processes"
    - "conditional generative neural system for probabilistic trajectory prediction"
    - "the functional neural process"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "neural processes"

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.CV(Computer Vision and Pattern Recognition)