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 "optimizing city scale traffic flows through modeling isolated observations of vehicle movements"):
  Abstract: Mobile phones and the Internet of Things provide unprecedented opportunities for transportation researchers and computational social scientists to observe city-scale human dynamics in terms of millions of vehicles or people moving around. They also enable policy researchers to identify the best strategies to influence the individuals in order for the complex system to achieve the best utility. However, the mobility data become sparse at the individual level and it is non-trivial to stitch together the isolated observations with high fidelity models to infer the macroscopic dynamics. In this paper, we introduce a discrete-event decision process to capture the high fidelity dynamics of a complex system at the individual level in terms of a collection of microscopic events where each one brings minimum changes but together induce complex behaviors. We further derive a particle filter algorithm to connect the dots of isolated observations through driving the discrete-event decision process in agreement with these observations. Finally, we solve a partially observable Markov decision process problem through reducing it into a learning and inference task. Evaluation with one synthesized dataset (SynthTown), one partly real and partly synthesized dataset (Berlin), and three real world datasets (Santiago de Chile, Dakar, and NYC) show that the discrete-event decision process gives an accurate estimation of complex system dynamics due to its better integration of high-fidelity dynamics and human mobility data.

Includes members:
  Member "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "physical virtual collaboration graph network for station level metro ridership prediction")
  Member "deep spatio temporal residual networks for citywide crowd flows prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "d4d senegal the second mobile phone data for development challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "better coverage better outcomes mapping mobile network data to official statistics using satellite imagery and radio propagation modelling")
  Member "exploring universal patterns in human home work commuting from mobile phone data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "transfer learning from an auxiliary discriminative task for unsupervised anomaly detection")
  Member "continuous time bayesian networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gru ode bayes continuous modeling of sporadically observed time series")
  Member "prediction expectation and surprise methods designs and study of a deployed traffic forecasting service":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what does the crowd say about you evaluating aggregation based location privacy")

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.SI(Social and Information Networks)