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 "two stream multi channel convolutional neural network tm cnn for multi lane traffic speed prediction considering traffic volume impact"):
  Abstract: Traffic speed prediction is a critically important component of intelligent transportation systems (ITS). Recently, with the rapid development of deep learning and transportation data science, a growing body of new traffic speed prediction models have been designed, which achieved high accuracy and large-scale prediction. However, existing studies have two major limitations. First, they predict aggregated traffic speed rather than lane-level traffic speed; second, most studies ignore the impact of other traffic flow parameters in speed prediction. To address these issues, we propose a two-stream multi-channel convolutional neural network (TM-CNN) model for multi-lane traffic speed prediction considering traffic volume impact. In this model, we first introduce a new data conversion method that converts raw traffic speed data and volume data into spatial-temporal multi-channel matrices. Then we carefully design a two-stream deep neural network to effectively learn the features and correlations between individual lanes, in the spatial-temporal dimensions, and between speed and volume. Accordingly, a new loss function that considers the volume impact in speed prediction is developed. A case study using one-year data validates the TM-CNN model and demonstrates its superiority. This paper contributes to two research areas: (1) traffic speed prediction, and (2) multi-lane traffic flow study.

Includes members:
  Member "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "interpretable crowd flow prediction with spatial temporal self attention")
  Member "forecasting transportation network speed using deep capsule networks with nested lstm models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for spatio temporal data mining a survey")
  Member "revisiting spatial temporal similarity a deep learning framework for traffic prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "demand forecasting from spatiotemporal data with graph networks and temporal guided embedding")
  Member "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "multistep speed prediction on traffic networks a graph convolutional sequence to sequence learning approach with attention mechanism":
    also connected through other hyperedges:
      (none)
  Member "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a neural architecture for detecting confusion in eye tracking data")

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)