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 "trajedi trajectory dissimilarity"):
  Abstract: The vast increase in our ability to obtain and store trajectory data necessitates trajectory analytics techniques to extract useful information from this data. In fact, trajectory analysis is an essential function in intelligent transportation systems (ITS) and by applying it for the spatial trajectory data, a wide range of transportation problems can be solved. Pair-wise distance functions are a foundation building block for common operations on trajectory datasets including constrained SELECT queries, k-nearest neighbors, and similarity and diversity algorithms. The accuracy and performance of these operations depend heavily on the speed and accuracy of the underlying trajectory distance function, which is in turn affected by trajectory calibration. Current methods either require calibrated data or perform calibration of the entire relevant dataset first, which is expensive and time consuming for large datasets. We present TRAJEDI, a calibration aware pair-wise distance calculation scheme that outperforms naive approaches while preserving accuracy. We also provide analyses of parameter tuning to trade off between speed and accuracy. Our scheme is usable with any diversity, similarity or k-nearest neighbor algorithm.

Includes members:
  Member "detecting community structure in dynamic social networks using the concept of leadership":
    also connected through other hyperedges:
      (none)
  Member "using phone sensors and an artificial neural network to detect gait changes during drinking episodes in the natural environment":
    also connected through other hyperedges:
      (none)
  Member "an artificial neural network for movement pattern analysis to estimate blood alcohol content level":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deforestation prediction using neural networks and satellite imagery in a spatial information system")

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)