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 "building effective large scale traffic state prediction system traffic4cast challenge solution"):
  Abstract: How to build an effective large-scale traffic state prediction system is a challenging but highly valuable problem. This study focuses on the construction of an effective solution designed for spatio-temporal data to predict large-scale traffic state. Considering the large data size in Traffic4cast Challenge and our limited computational resources, we emphasize model design to achieve a relatively high prediction performance within acceptable running time. We adopt a structure similar to U-net and use a mask instead of spatial attention to address the data sparsity. Then, combined with the experience of time series prediction problem, we design a number of features, which are input into the model as different channels. Region cropping is used to decrease the difference between the size of the receptive field and the study area, and the models can be specially optimized for each sub-region. The fusion of interdisciplinary knowledge and experience is an emerging demand in classical traffic research. Several interdisciplinary studies we have been studying are also discussed in the Complementary Challenges. The source codes are available in this https URL.

Includes members:
  Member "deep sequence learning with auxiliary information for traffic prediction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning for spatio temporal data mining a survey")
  Member "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "deep multi view spatial temporal network for taxi demand prediction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "revisiting convolutional neural networks for urban flow analytics")
  Member "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "interpretable crowd flow prediction with spatial temporal self attention")
  Member "deep spatio temporal residual networks for citywide crowd flows prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)