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: "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
  Abstract: Traffic forecasting is a challenging task, due to the complicated spatial dependencies on roadway networks and the time-varying traffic patterns. To address this challenge, we learn the traffic network as a graph and propose a novel deep learning framework, High-Order Graph Convolutional Long Short-Term Memory Neural Network (HGC-LSTM), to learn the interactions between links in the traffic network and forecast the network-wide traffic state. We define the high-order traffic graph convolution based on the physical network topology. The proposed framework employs L1-norms on the graph convolution weights and L2-norms on the graph convolution features to identify the most influential links in the traffic network. We propose a novel Real-Time Branching Learning (RTBL) algorithm for the HGC-LSTM framework to accelerate the training process for spatio-temporal data. Experiments show that our HGC-LSTM network is able to capture the complex spatio-temporal dependencies efficiently present in the traffic network and consistently outperforms state-of-the-art baseline methods on two heterogeneous real-world traffic datasets. The visualization of graph convolution weights shows that the proposed framework can accurately recognize the most influential roadway segments in real-world traffic networks.

Connected through hyperedge HE-1 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction"):
  with members:
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "graph convolutional label noise cleaner train a plug and play action classifier for anomaly detection"
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning semantic specific graph representation for multi label image recognition"
    - "graph convolutional neural networks for web scale recommender systems"
    - "graph to sequence learning using gated graph neural networks"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "hybrid knowledge routed modules for large scale object detection"

Connected through hyperedge HE-2 (induced by source paper "using deep learning to extend the range of air pollution monitoring and forecasting"):
  with members:
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "fd net with auxiliary time steps fast prediction of pdes using hessian free trust region methods"
    - "hidden physics models machine learning of nonlinear partial differential equations"
    - "latent space physics towards learning the temporal evolution of fluid flow"
    - "neural ordinary differential equations"
    - "deep air learning interpolation prediction and feature analysis of fine grained air quality"
    - "machine learning of linear differential equations using gaussian processes"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"

Connected through hyperedge HE-3 (induced by source paper "ddp gcn multi graph convolutional network for spatiotemporal traffic forecasting"):
  with members:
    - "multi modal graph interaction for multi graph convolution network in urban spatiotemporal forecasting"
    - "st unet a spatio temporal u network for graph structured time series modeling"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "semi supervised classification with graph convolutional networks"
    - "sequence to sequence learning with neural networks"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-4 (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-5 (induced by source paper "graph neural ordinary differential equations"):
  with members:
    - "universal differential equations for scientific machine learning"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "latent odes for irregularly sampled time series"
    - "graph neural networks exponentially lose expressive power for node classification"
    - "anode unconditionally accurate memory efficient gradients for neural odes"
    - "hamiltonian graph networks with ode integrators"
    - "gresnet graph residual network for reviving deep gnns from suspended animation"
    - "fast and deep graph neural networks"

Connected through hyperedge HE-6 (induced by source paper "forecasting trajectory and behavior of road agents using spectral clustering in graph lstms"):
  with members:
    - "grip graph based interaction aware trajectory prediction"
    - "robusttp end to end trajectory prediction for heterogeneous road agents in dense traffic with noisy sensor inputs"
    - "sr lstm state refinement for lstm towards pedestrian trajectory prediction"
    - "self driving cars a survey"
    - "multi agent tensor fusion for contextual trajectory prediction"
    - "predicting motion of vulnerable road users using high definition maps and efficient convnets"
    - "a behavior driven approach for sampling rare event situations for autonomous vehicles"
    - "nuscenes a multimodal dataset for autonomous driving"

Connected through hyperedge HE-7 (induced by source paper "a capsule network based model for learning node embeddings"):
  with members:
    - "graph convolutional neural networks for web scale recommender systems"
    - "a tutorial on network embeddings"
    - "relational inductive biases deep learning and graph networks"
    - "spine structural identity preserved inductive network embedding"
    - "billion scale commodity embedding for e commerce recommendation in alibaba"
    - "network representation learning a survey"
    - "dynamic routing between capsules"
    - "representation learning on graphs methods and applications"

Connected through hyperedge HE-8 (induced by source paper "modeling combinatorial evolution in time series prediction"):
  with members:
    - "relational neural expectation maximization unsupervised discovery of objects and their interactions"
    - "unsupervised anomaly detection via variational auto encoder for seasonal kpis in web applications"
    - "graph networks as learnable physics engines for inference and control"
    - "multilevel wavelet decomposition network for interpretable time series analysis"
    - "relational inductive bias for physical construction in humans and machines"
    - "action schema networks generalised policies with deep learning"
    - "toeplitz inverse covariance based clustering of multivariate time series data"
    - "neural message passing for quantum chemistry"

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)