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 hybrid method for traffic flow forecasting using multimodal deep learning"
  Abstract: Traffic flow forecasting has been regarded as a key problem of intelligent transport systems. In this work, we propose a hybrid multimodal deep learning method for short-term traffic flow forecasting, which can jointly and adaptively learn the spatial-temporal correlation features and long temporal interdependence of multi-modality traffic data by an attention auxiliary multimodal deep learning architecture. According to the highly nonlinear characteristics of multi-modality traffic data, the base module of our method consists of one-dimensional Convolutional Neural Networks (1D CNN) and Gated Recurrent Units (GRU) with the attention mechanism. The former is to capture the local trend features and the latter is to capture the long temporal dependencies. Then, we design a hybrid multimodal deep learning framework (HMDLF) for fusing share representation features of different modality traffic data by multiple CNN-GRU-Attention modules. The experimental results indicate that the proposed multimodal deep learning model is capable of dealing with complex nonlinear urban traffic flow forecasting with satisfying accuracy and effectiveness.

Connected through hyperedge HE-1 (induced by source paper "deep learning for spatio temporal data mining a survey"):
  with members:
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "a comprehensive survey on graph neural networks"
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "where to go next a spatio temporal lstm model for next poi recommendation"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "deep sequence learning with auxiliary information for traffic prediction"
    - "efficient metropolitan traffic prediction based on graph recurrent neural network"

Connected through hyperedge HE-2 (induced by source paper "graph partitioning based diffusion convolution recurrent neural network for large scale traffic forecasting"):
  with members:
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "multi task learning as multi objective optimization"
    - "character level language modeling with deeper self attention"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "cascade r cnn delving into high quality object detection"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "an empirical comparison of the summarization power of graph clustering methods"

Connected through hyperedge HE-3 (induced by source paper "urban flows prediction from spatial temporal data using machine learning a survey"):
  with members:
    - "bike flow prediction with multi graph convolutional networks"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "spatio temporal data mining a survey of problems and methods"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "predicting citywide crowd flows using deep spatio temporal residual networks"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "amharic arabic neural machine translation"):
  with members:
    - "opennmt open source toolkit for neural machine translation"
    - "six challenges for neural machine translation"
    - "first result on arabic neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "effective approaches to attention based neural machine translation"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "neural machine translation by jointly learning to align and translate"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"

Connected through hyperedge HE-5 (induced by source paper "dynamic spatio temporal graph based cnns for traffic prediction"):
  with members:
    - "deep sequence learning with auxiliary information for traffic prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "deeptrend a deep hierarchical neural network for traffic flow prediction"
    - "short term traffic flow forecasting with spatial temporal correlation in a hybrid deep learning framework"
    - "ssd single shot multibox detector"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"

Connected through hyperedge HE-6 (induced by source paper "travel speed prediction with a hierarchical convolutional neural network and long short term memory model framework"):
  with members:
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "rethinking the inception architecture for computer vision"

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.LG(Machine Learning)