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: "deeptravel a neural network based travel time estimation model with auxiliary supervision"
  Abstract: Estimating the travel time of a path is of great importance to smart urban mobility. Existing approaches are either based on estimating the time cost of each road segment which are not able to capture many cross-segment complex factors, or designed heuristically in a non-learning-based way which fail to utilize the existing abundant temporal labels of the data, i.e., the time stamp of each trajectory point. In this paper, we leverage on new development of deep neural networks and propose a novel auxiliary supervision model, namely DeepTravel, that can automatically and effectively extract different features, as well as make full use of the temporal labels of the trajectory data. We have conducted comprehensive experiments on real datasets to demonstrate the out-performance of DeepTravel over existing approaches.

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"
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "deep sequence learning with auxiliary information for traffic prediction"

Connected through hyperedge HE-2 (induced by source paper "deeply supervised knowledge synergy"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "knowledge transfer with jacobian matching"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "deep supervision with intermediate concepts"
    - "deep co training for semi supervised image recognition"
    - "modality distillation with multiple stream networks for action recognition"
    - "efficient neural architecture search via parameter sharing"

Connected through hyperedge HE-3 (induced by source paper "deepist deep image based spatio temporal network for travel time estimation"):
  with members:
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "lstm a search space odyssey"
    - "deep sentence embedding using long short term memory networks analysis and application to information retrieval"
    - "unsupervised learning of video representations using lstms"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "a simple baseline for travel time estimation using large scale trip data"
    - "adam a method for stochastic optimization"
    - "convolutional neural networks for sentence classification"

Connected through hyperedge HE-4 (induced by source paper "map enhanced route travel time prediction using deep neural networks"):
  with members:
    - "node2vec scalable feature learning for networks"

Connected through hyperedge HE-5 (induced by source paper "travel time estimation without road networks an urban morphological layout representation approach"):
  with members:
    - "using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale"

Connected through hyperedge HE-6 (induced by source paper "recurrent multi graph neural networks for travel cost prediction"):
  with members:
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "geometric matrix completion with recurrent multi graph neural networks"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "learning phrase representations using rnn encoder decoder for statistical machine translation"
    - "sequence to sequence learning with neural networks"
    - "spectral networks and locally connected networks on graphs"

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)