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 "one pass trajectory simplification using the synchronous euclidean distance"):
  Abstract: Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. To attack this issue, one-pass line simplification (LS) algorithms have are been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance (SED), and cannot support spatio-temporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms (CISED-S and CISED-W) using SED, based on a novel spatio-temporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms CISED-S and CISED-W are on average 3 times faster than SQUISH-E (the most efficient existing LS algorithm using SED). In terms of compression ratios, algorithms CISED-S and CISED-W are comparable with and 19.6% better than DPSED (the most effective existing LS algorithm using SED) on average, respectively, and are 21.1% and 42.4% better than SQUISH-E on average, respectively.

Includes members:
  Member "one pass error bounded trajectory simplification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximate computation for big data analytics")
  Member "a novel framework for online amnesic trajectory compression in resource constrained environments":
    also connected through other hyperedges:
      (none)
  Member "press a novel framework of trajectory compression in road networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "gract a grammar based compressed index for trajectory data")
  Member "bounded quadrant system error bounded trajectory compression on the go":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approximate computation for big data analytics")

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.DB(Databases)