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 "ride sharing and dynamic networks analysis"):
  Abstract: The potential of an efficient ride-sharing scheme to significantly reduce traffic congestion, lower emission level, as well as facilitating the introduction of smart cities has been widely demonstrated. This positive thrust however is faced with several delaying factors, one of which is the volatility and unpredictability of the potential benefit (or utilization) of ride-sharing at different times, and in different places. In this work the following research questions are posed: (a) Is ride-sharing utilization stable over time or does it undergo significant changes? (b) If ride-sharing utilization is dynamic, can it be correlated with some traceable features of the traffic? and (c) If ride-sharing utilization is dynamic, can it be predicted ahead of time? We analyze a dataset of over 14 Million taxi trips taken in New York City. We propose a dynamic travel network approach for modeling and forecasting the potential ride-sharing utilization over time, showing it to be highly volatile. In order to model the utilization's dynamics we propose a network-centric approach, projecting the aggregated traffic taken from continuous time periods into a feature space comprised of topological features of the network implied by this traffic. This feature space is then used to model the dynamics of ride-sharing utilization over time. The results of our analysis demonstrate the significant volatility of ride-sharing utilization over time, indicating that any policy, design or plan that would disregard this aspect and chose a static paradigm would undoubtably be either highly inefficient or provide insufficient resources. We show that using our suggested approach it is possible to model the potential utilization of ride sharing based on the topological properties of the rides network. We also show that using this method the potential utilization can be forecasting a few hours ahead of time.

Includes members:
  Member "optimal dynamic coverage infrastructure for large scale fleets of reconnaissance uavs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "self organizing relay selection in uav communication networks a matching game perspective")
  Member "predictive positioning and quality of service ridesharing for campus mobility on demand systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a probabilistic approach for demand aware ride sharing optimization")
  Member "human collective intelligence as distributed bayesian inference":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "social learning in multi agent multi armed bandits")
  Member "an analysis of issues against the adoption of dynamic carpooling":
    also connected through other hyperedges:
      (none)
  Member "the cooperative cleaners problem in stochastic dynamic environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "optimal dynamic coverage infrastructure for large scale fleets of reconnaissance uavs")
  Member "incremental learning with accuracy prediction of social and individual properties from mobile phone data":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "optimal dynamic coverage infrastructure for large scale fleets of reconnaissance uavs")
  Member "static and expanding grid coverage with ant robots complexity results":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "optimal dynamic coverage infrastructure for large scale fleets of reconnaissance uavs")

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)