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: "taxi demand forecasting a hedge based tessellation strategy for improved accuracy"
  Abstract: A key problem in location-based modeling and forecasting lies in identifying suitable spatial and temporal resolutions. In particular, judicious spatial partitioning can play a significant role in enhancing the performance of location-based forecasting models. In this paper, we investigate two widely used tessellation strategies for partitioning city space, in the context of real-time taxi demand forecasting. Our study compares 1) the Geohash tessellation and 2) the Voronoi tessellation, using two distinct taxi demand data sets, over multiple time scales. For the purpose of comparison, we employ classical time-series tools to model the spatio-temporal demand. Our study finds that the performance of each tessellation strategy is highly dependent on the city geography, spatial distribution of the data, and the time of the day, and that neither strategy is found to perform optimally across the forecast horizon. We propose a combining algorithm that selects the best tessellation strategy at each time step, based on their recent performance. Our algorithm is a non-stationary variant of the well-known HEDGE algorithm for choosing the best advice from multiple experts. We show that the proposed strategy performs consistently better than either of the two tessellation strategies across the data sets considered, at multiple time scales, and with different performance metrics. We achieved an average accuracy of above 80% per km2 for both data sets considered at 60 min aggregation levels.

Connected through hyperedge HE-1 (induced by source paper "demand forecasting from spatiotemporal data with graph networks and temporal guided embedding"):
  with members:
    - "predicting urban dispersal events a two stage framework through deep survival analysis on mobility data"
    - "revisiting spatial temporal similarity a deep learning framework for traffic prediction"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "tensor fusion network for multimodal sentiment analysis"
    - "attention is all you need"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "spatiotemporal recurrent convolutional networks for traffic prediction in transportation networks"
    - "modeling long and short term temporal patterns with deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "grids versus graphs partitioning space for improved taxi demand supply forecasts"):
  with members:
    - "recurrent neural networks for time series forecasting"
    - "a comprehensive survey on graph neural networks"
    - "taxi demand supply forecasting impact of spatial partitioning on the performance of neural networks"
    - "dynamic spatio temporal graph based cnns for traffic prediction"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "high order graph convolutional recurrent neural network a deep learning framework for network scale traffic learning and forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "an aggregating strategy for shifting experts in discrete sequence prediction"

Connected through hyperedge HE-3 (induced by source paper "a framework for end to end deep learning based anomaly detection in transportation networks"):
  with members:
    - "one class convolutional neural network"
    - "lstm based anomaly detection detection rules from extreme value theory"
    - "deep learning for anomaly detection a survey"
    - "deep anomaly detection using geometric transformations"
    - "anomaly detection using one class neural networks"
    - "lstm based encoder decoder for multi sensor anomaly detection"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-4 (induced by source paper "lstm based anomaly detection detection rules from extreme value theory"):
  with members:
    - "deep learning for anomaly detection a survey"
    - "detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding"
    - "anomaly detection for a water treatment system using unsupervised machine learning"
    - "recurrent neural radio anomaly detection"

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)