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 "faster fusion analytics for public transport event response"):
  Abstract: Increasing urban concentration raises operational challenges that can benefit from integrated monitoring and decision support. Such complex systems need to leverage the full stack of analytical methods, from state estimation using multi-sensor fusion for situational awareness, to prediction and computation of optimal responses. The FASTER platform that we describe in this work, deployed at nation scale and handling 1.5 billion public transport trips a year, offers such a full stack of techniques for this large-scale, real-time problem. FASTER provides fine-grained situational awareness and real-time decision support with the objective of improving the public transport commuter experience. The methods employed range from statistical machine learning to agent-based simulation and mixed-integer optimization. In this work we present an overview of the challenges and methods involved, with details of the commuter movement prediction module, as well as a discussion of open problems.

Includes members:
  Member "robust commuter movement inference from connected mobile devices":
    also connected through other hyperedges:
      (none)
  Member "stochastic on time arrival problem in transit networks":
    also connected through other hyperedges:
      (none)
  Member "early warning of human crowds based on query data from baidu maps analysis based on shanghai stampede":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the impact of position errors on crowd simulation")
  Member "trajectorynet an embedded gps trajectory representation for point based classification using recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for spatio temporal data mining a survey")

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.CY(Computers and Society)