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 "inertial single vehicle trajectory prediction baselines and applications with the ngsim dataset"):
  Abstract: In the recent vehicle trajectory prediction literature, the most common baselines are briefly introduced without the necessary information to reproduce it. In this article we produce reproducible vehicle prediction results from simple models. For that purpose, the process is explicit, and the code is available. Those baseline models are a constant velocity model and a single vehicle prediction model. They are applied on the NGSIM US-101 and I-80 datasets using only relative positions. Thus, the process can be reproduced with any database containing tracking of vehicle positions. Produced results on this database establish the three most used trajectory prediction performance indicators: Root Mean Squared Error (RMSE), Negative Log-Likelihood (NLL), and Mean Absolute Error (MAE). The NLL estimation needs attention because several formulations that differ from the mathematical definition are used in other works. This article is meant to be used along with the published code to establish baselines for further work.

Includes members:
  Member "interaction aware kalman neural networks for trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "visionnet a drivable space based interactive motion prediction network for autonomous driving")
  Member "on the variance of the adaptive learning rate and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "learning trajectory prediction with continuous inverse optimal control via langevin sampling of energy based models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "integrating planning and interpretable goal recognition for autonomous driving")
  Member "lookahead optimizer k steps forward 1 step back":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "udd an underwater open sea farm object detection dataset for underwater robot picking")
  Member "convolutional social pooling for vehicle trajectory prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sequential forecasting of 100 000 points")
  Member "long short term memory kalman filters recurrent neural estimators for pose regularization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the apolloscape open dataset for autonomous driving and its application")

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.RO(Robotics)