human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "cmts conditional multiple trajectory synthesizer for generating safety critical driving scenarios"):
  Abstract: Naturalistic driving trajectories are crucial for the performance of autonomous driving algorithms. However, most of the data is collected in safe scenarios leading to the duplication of trajectories which are easy to be handled by currently developed algorithms. When considering safety, testing algorithms in near-miss scenarios that rarely show up in off-the-shelf datasets is a vital part of the evaluation. As a remedy, we propose a near-miss data synthesizing framework based on Variational Bayesian methods and term it as Conditional Multiple Trajectory Synthesizer (CMTS). We leverage a generative model conditioned on road maps to bridge safe and collision driving data by representing their distribution in the latent space. By sampling from the near-miss distribution, we can synthesize safety-critical data crucial for understanding traffic scenarios but not shown in neither the original dataset nor the collision dataset. Our experimental results demonstrate that the augmented dataset covers more kinds of driving scenarios, especially the near-miss ones, which help improve the trajectory prediction accuracy and the capability of dealing with risky driving scenarios.

Includes members:
  Member "fully featured attribute transfer":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "clsgan selective attribute editing based on classification adversarial network")
  Member "a multi domain feature learning method for visual place recognition":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "retrieval based localization based on domain invariant feature learning under changing environments")
  Member "a closed form solution to universal style transfer":
    also connected through other hyperedges:
      (none)
  Member "online vehicle trajectory prediction using policy anticipation network and optimization based context reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "formulazero distributionally robust online adaptation via offline population synthesis")
  Member "interpolation in generative models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "segma semi supervised gaussian mixture auto encoder")
  Member "nuscenes a multimodal dataset for autonomous driving":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "review deep learning on 3d point clouds")
  Member "predicting vehicle behaviors over an extended horizon using behavior interaction network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient uncertainty aware decision making for automated driving using guided branching")
  Member "interaction aware multi agent tracking and probabilistic behavior prediction via adversarial learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "social wagdat interaction aware trajectory prediction via wasserstein graph double attention network")

Question: Please tell me which class the source paper 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)