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 "netclus a scalable framework for locating top k sites for placement of trajectory aware services"):
  Abstract: Facility location queries identify the best locations to set up new facilities for providing service to its users. Majority of the existing works in this space assume that the user locations are static. Such limitations are too restrictive for planning many modern real-life services such as fuel stations, ATMs, convenience stores, cellphone base-stations, etc. that are widely accessed by mobile users. The placement of such services should, therefore, factor in the mobility patterns or trajectories of the users rather than simply their static locations. In this work, we introduce the TOPS (Trajectory-Aware Optimal Placement of Services) query that locates the best k sites on a road network. The aim is to optimize a wide class of objective functions defined over the user trajectories. We show that the problem is NP-hard and even the greedy heuristic with an approximation bound of (1-1/e) fails to scale on urban-scale datasets. To overcome this challenge, we develop a multi-resolution clustering based indexing framework called NetClus. Empirical studies on real road network trajectory datasets show that NetClus offers solutions that are comparable in terms of quality with those of the greedy heuristic, while having practical response times and low memory footprints. Additionally, the NetClus framework can absorb dynamic updates in mobility patterns, handle constraints such as site-costs and capacity, and existing services, thereby providing an effective solution for modern urban-scale scenarios.

Includes members:
  Member "trajectory aware macro cell planning for mobile users":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "what is lte actually used for an answer through multi operator crowd sourced measurement")
  Member "inferring uncertain trajectories from partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint modeling of dense and incomplete trajectories for citywide traffic volume inference")
  Member "discovery of convoys in trajectory databases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "finding route hotspots in large labeled networks")

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)