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: "learning from multiple cities a meta learning approach for spatial temporal prediction"
  Abstract: Spatial-temporal prediction is a fundamental problem for constructing smart city, which is useful for tasks such as traffic control, taxi dispatching, and environmental policy making. Due to data collection mechanism, it is common to see data collection with unbalanced spatial distributions. For example, some cities may release taxi data for multiple years while others only release a few days of data; some regions may have constant water quality data monitored by sensors whereas some regions only have a small collection of water samples. In this paper, we tackle the problem of spatial-temporal prediction for the cities with only a short period of data collection. We aim to utilize the long-period data from other cities via transfer learning. Different from previous studies that transfer knowledge from one single source city to a target city, we are the first to leverage information from multiple cities to increase the stability of transfer. Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm. The meta-learning paradigm learns a well-generalized initialization of the spatial-temporal network, which can be effectively adapted to target cities. In addition, a pattern-based spatial-temporal memory is designed to distill long-term temporal information (i.e., periodicity). We conduct extensive experiments on two tasks: traffic (taxi and bike) prediction and water quality prediction. The experiments demonstrate the effectiveness of our proposed model over several competitive baseline models.

Connected through hyperedge HE-1 (induced by source paper "transferring robustness for graph neural network against poisoning attacks"):
  with members:
    - "adversarial examples on graph data deep insights into attack and defense"
    - "power up robust graph convolutional network against evasion attacks based on graph powering"
    - "topology attack and defense for graph neural networks an optimization perspective"
    - "graph neural networks with generated parameters for relation extraction"
    - "graph few shot learning via knowledge transfer"
    - "graph convolutional networks with eigenpooling"
    - "graph neural networks with high order feature interactions"
    - "regional homogeneity towards learning transferable universal adversarial perturbations against defenses"

Connected through hyperedge HE-2 (induced by source paper "automated relational meta learning"):
  with members:
    - "few shot unsupervised image to image translation"
    - "tafe net task aware feature embeddings for low shot learning"
    - "metapred meta learning for clinical risk prediction with limited patient electronic health records"
    - "tapnet neural network augmented with task adaptive projection for few shot learning"
    - "hierarchically structured meta learning"
    - "on first order meta learning algorithms"
    - "toward multimodal model agnostic meta learning"
    - "reptile a scalable metalearning algorithm"

Connected through hyperedge HE-3 (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"
    - "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"
    - "efficient metropolitan traffic prediction based on graph recurrent neural network"

Connected through hyperedge HE-4 (induced by source paper "dynamic spatial temporal representation leaning for crowd flow prediction"):
  with members:
    - "contextualized spatial temporal network for taxi origin destination demand prediction"
    - "crowd counting using deep recurrent spatial aware network"
    - "cross city transfer learning for deep spatio temporal prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "attentive crowd flow machines"
    - "crowd flow prediction by deep spatio temporal transfer learning"
    - "visual analytics of movement pattern based on time spatial data a neural net approach"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"

Connected through hyperedge HE-5 (induced by source paper "graph few shot learning via knowledge transfer"):
  with members:
    - "transferring robustness for graph neural network against poisoning attacks"
    - "transferable end to end aspect based sentiment analysis with selective adversarial learning"
    - "meta dataset a dataset of datasets for learning to learn from few examples"
    - "hierarchically structured meta learning"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "geniepath graph neural networks with adaptive receptive paths"
    - "large scale learnable graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "few shot knowledge graph completion"):
  with members:
    - "transferable end to end aspect based sentiment analysis with selective adversarial learning"
    - "hierarchically structured meta learning"
    - "probabilistic model agnostic meta learning"
    - "one shot relational learning for knowledge graphs"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "prototypical networks for few shot learning"
    - "meta sgd learning to learn quickly for few shot learning"
    - "learning to compare relation network for few shot learning"

Connected through hyperedge HE-7 (induced by source paper "joint modeling of local and global temporal dynamics for multivariate time series forecasting with missing values"):
  with members:
    - "megan a generative adversarial network for multi view network embedding"
    - "joint modeling of dense and incomplete trajectories for citywide traffic volume inference"
    - "brits bidirectional recurrent imputation for time series"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "a memory network based solution for multivariate time series forecasting"
    - "a dual stage attention based recurrent neural network for time series prediction"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "end to end learning for short text expansion"

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)