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 "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.

Includes members:
  Member "learning to propagate labels transductive propagation network for few shot learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "revisiting spatial temporal similarity a deep learning framework for traffic prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "demand forecasting from spatiotemporal data with graph networks and temporal guided embedding")
  Member "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "cross city transfer learning for deep spatio temporal prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "dynamic spatial temporal representation leaning for crowd flow prediction")
  Member "deep multi view spatial temporal network for taxi demand prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "revisiting convolutional neural networks for urban flow analytics")
  Member "gaan gated attention networks for learning on large and spatiotemporal graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "transferring robustness for graph neural network against poisoning attacks")
  Member "crowd flow prediction by deep spatio temporal transfer learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dynamic spatial temporal representation leaning for crowd flow prediction")
  Member "diffusion convolutional recurrent neural network data driven traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "relational state space model for stochastic multi object systems")

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)